{"as_of":"2026-08-14T00:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:69a24bb1bb5335de61b3006cab3daa21656d7c9120cd742a0a9f6e686b751a2d","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:20:16.672395Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.18377/citation-record","integrity":"/paper/2411.18377/integrity","json":"/paper/2411.18377/citation-record.json","paper":"/paper/2411.18377"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.742322Z","title":"https://www.meta.com/en-gb/blog/ quest / ai - powered - technologies - quest - 3 - pro-ray-ban-meta-smart-glasses/","venue":null,"work_id":"d49615a7-257a-40ea-9fd9-0cc5f7125cca","year":2024},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.349204Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:13fc3b3dd7441b8c259a6ec0113adb38283622eb8075a9644718a1e2544fdbd5","observation_id":"7b495109-4b19-4742-a208-b43dedc74240","resolution":{"observed_at":"2026-08-12T11:20:17.748072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.724796Z","title":"Un- realego: A new dataset for robust egocentric 3d human mo- tion capture","venue":null,"work_id":"85026fd0-6e21-4ee5-8c01-1e6fb3820714","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.354779Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:e75475e10f9f570c7d9214a249dfafea3f78d182df51e37f11a6c0100c383351","observation_id":"8c997807-3de1-43af-be02-20691dc9ca12","resolution":{"observed_at":"2026-08-12T11:20:17.730690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.704826Z","title":"Flag: Flow- based 3d avatar generation from sparse observations","venue":null,"work_id":"b2dbc615-e819-4a0e-8af0-b74a78df1532","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.360196Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:90682c57594409654c77180dbad5c001f0c086627aac4c56bb633245266a96e1","observation_id":"25d40860-9d22-4344-a85c-46b73eb9f8e4","resolution":{"observed_at":"2026-08-12T11:20:17.710472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.687239Z","title":"A stochastic con- ditioning scheme for diverse human motion prediction","venue":null,"work_id":"1d2fc1a7-a199-4072-a83c-4848bd5ba8f8","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.365141Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:27a88649d7a62bbcaf6052f73cdfdf0c4b535a168091fd983ea527c446c3b88b","observation_id":"4e99a7ed-3ba8-4162-9df4-154c30bd2bfd","resolution":{"observed_at":"2026-08-12T11:20:17.692267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.667676Z","title":"Fast on-board 3d torso pose recovery and forecasting","venue":null,"work_id":"679faa8b-a122-4294-989d-5d8770d3320d","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.372046Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:708da262cd0cc72913d3a03c125e9e1b3d740cc4a9ecb23ebf32e2fdb07fcf3a","observation_id":"7c0881cb-b235-46f3-bec5-24667e3268ce","resolution":{"observed_at":"2026-08-12T11:20:17.674500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.650364Z","title":null,"venue":null,"work_id":"5778e2a6-1bc4-4890-88df-35bfcbd6980d","year":2018},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.377542Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:245a293291a12058b59dae26922c7793bc767239b3b4314b41859e563096c1d0","observation_id":"f06c614a-b1eb-4d31-86c2-d2bdb8785a9d","resolution":{"observed_at":"2026-08-12T11:20:17.655817Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.633429Z","title":"Passthrough+ real-time stereoscopic view synthesis for mo- bile mixed reality","venue":null,"work_id":"1f3371ae-faa9-4a98-b8f4-958e583cc50d","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.383302Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:8d2e059982722712f2041a05eb29a2b5e0c4c6e89410ae1576b1d60479950ac7","observation_id":"a8be0671-0332-4886-93ca-c8155c2012eb","resolution":{"observed_at":"2026-08-12T11:20:17.638852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.615515Z","title":"Refinet: 3d human pose re- finement with depth maps","venue":null,"work_id":"5ec5ba9f-0687-4595-b1a7-08afc5c9c2c5","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.388731Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:a2f044838964d52697de3c0ac2e62e57c2a4341a899b9eff7b64fa328dcfb9b6","observation_id":"3038ac5a-d6b5-45c0-83fb-10203618bcca","resolution":{"observed_at":"2026-08-12T11:20:17.621776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.597305Z","title":"Recent advances and perspectives in deep learning techniques for 3d point cloud data process- ing","venue":null,"work_id":"29577273-1047-430b-bd8c-f4f9926e9d62","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.393869Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:5b2b88a60647dc08de287b8a7df0bf7c802c03b8154ea7446b99a955034b307d","observation_id":"b4770249-b37f-4523-b9d4-ca3cfe4d8be9","resolution":{"observed_at":"2026-08-12T11:20:17.603872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.579799Z","title":"Full-body motion from a single head-mounted device: Generating smpl poses from partial observations","venue":null,"work_id":"9579dcd8-75c7-4b16-a377-5111e6eb8dcb","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.398923Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:95c804a458d80643a07fed93c09b0c13662a2f989114260f536f004833e3d9ec","observation_id":"993f6f66-9cac-4937-81c6-06b82936f595","resolution":{"observed_at":"2026-08-12T11:20:17.585027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.557845Z","title":null,"venue":null,"work_id":"8fe10fdd-b236-4738-babc-c91f961770e2","year":null},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.403673Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:6c8d631b5c93c105002adb8d2c9099b2933f2f623deda536e6303980b360db89","observation_id":"00bcd906-8b60-4486-bbb7-f89037d4ed67","resolution":{"observed_at":"2026-08-12T11:20:17.564060Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.538750Z","title":"Recurrent network models for human dynam- ics","venue":null,"work_id":"bc27fa38-c4ef-41e7-a4ba-14ca9b27c3de","year":2015},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.409653Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:2a9c8dc12bbb14d3eb864ad65ec24192e4bb5b387940d16b4e3d05576973a3ce","observation_id":"bc418497-1db0-417f-aee8-27aef58ebb37","resolution":{"observed_at":"2026-08-12T11:20:17.545292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.414832Z","title":"Generating diverse and natural 3d human motions from text","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.414832Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:0686de05f8c1ac6dd077d4c125095931bfffbe6da6d3e0ab63fcd70d79e43164","observation_id":"12655058-27fa-4627-99ae-9bda0fbe7004","resolution":{"observed_at":"2026-08-12T11:20:16.414832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.508057Z","title":"Human motion prediction via spatio-temporal in- painting","venue":null,"work_id":"c30fe604-1632-4b2d-86d7-7a21a544f3c8","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.420068Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:bc9c37f0359f758f9f6bc96135440a7737e30bfa2cda1ace6a35556cee6487cd","observation_id":"c6d28635-ca21-42ea-9953-0b21ad6ddb74","resolution":{"observed_at":"2026-08-12T11:20:17.514395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.485070Z","title":"A deep learning framework for character motion synthesis and editing","venue":null,"work_id":"85cb6abf-30d3-4a13-9c48-b538295d179c","year":2016},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.426247Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:bf68106ebe94ab0fb0abcfa77f16da006083086466abc8c3eb68e0a79d552376","observation_id":"72a34e99-3bdf-4524-9ded-c630ee5f0c57","resolution":{"observed_at":"2026-08-12T11:20:17.493388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.465041Z","title":"Black, Otmar Hilliges, and Gerard Pons-Moll","venue":null,"work_id":"3bb562b3-1c40-4310-9b4e-6ee28132af7e","year":null},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.432021Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:acf453af3578ebdd49b15e5f8d2772fcb8bac8fa333adb35a349d9866e5015fa","observation_id":"439eba14-7663-4fc8-afd7-0e887c6ec6c8","resolution":{"observed_at":"2026-08-12T11:20:17.471189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.446577Z","title":"See- ing invisible poses: Estimating 3d body pose from egocentric video","venue":null,"work_id":"71bdfab8-5998-4e86-b219-579cb00e6894","year":2017},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.437461Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:6eadffe1fa0d501a6518edd47f25c33273422fedea1339a5ee3521c6008c8a20","observation_id":"3409f86a-b7d8-4f6f-a822-3636149e2356","resolution":{"observed_at":"2026-08-12T11:20:17.452105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.427889Z","title":"Avatarposer: Ar- ticulated full-body pose tracking from sparse motion sens- ing","venue":null,"work_id":"ecd4754a-4fcb-4615-887c-50729e1451db","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.442730Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:6255d5fafc1e04c93a251831f75bf8a439eb6c717cb9398036e9762d3ced6387","observation_id":"ee457298-4e4c-41ea-bf27-d2323c08b09e","resolution":{"observed_at":"2026-08-12T11:20:17.434204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.409431Z","title":"Transformer inertial poser: Real-time human motion reconstruction from sparse imus with simultaneous terrain generation","venue":null,"work_id":"0910e4ab-ba44-4381-8804-4d100f3b3875","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.448152Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:e0a3ef261e92b6364593d242177516fc47456facc21999c43806fccce354ba60","observation_id":"baeec54f-49c4-4527-a940-538dd73edb1c","resolution":{"observed_at":"2026-08-12T11:20:17.416015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.392566Z","title":"Ego3dpose: Capturing 3d cues from binocular egocentric views","venue":null,"work_id":"39e3debf-453a-4f7a-9ad1-49d399b40ebe","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.453459Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:656c77d2328f0d7f7e72af628180bc53a53028468fcaeb8954829c60599841a7","observation_id":"d368dd96-013f-492d-acb3-951aaed42f28","resolution":{"observed_at":"2026-08-12T11:20:17.397985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.368076Z","title":"Human motion denoising using attention-based bidirectional recur- rent neural network","venue":null,"work_id":"c4ecd13f-dd4d-4e54-8b1d-17671c1c8856","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.458852Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:7400f9f1c3a3a99869bd44c6264acb94fdb3f8f2a5898a0283a85540e97dbd91","observation_id":"6b9b4dcf-e13e-4c89-a68f-ce282439722a","resolution":{"observed_at":"2026-08-12T11:20:17.375163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.466552Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.466552Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:cbbf70690546d036c24546ad6e0018d4bafc53e4ac5ac2e38555253e332bdea3","observation_id":"1ae72bad-d7f6-4c58-aeb7-b95e244128c0","resolution":{"observed_at":"2026-08-12T11:20:16.466552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.340104Z","title":"Practical stereo matching via cascaded recurrent net- work with adaptive correlation","venue":null,"work_id":"883db0d8-6e51-4197-ae05-6ebc7c5aa919","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.472446Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:fbdcc11f8cc19de98ae9f734cb858c46d101fcd8f4a9a88e12dda19953beaf57","observation_id":"13bf406b-2ecb-480c-b382-eb1e39f5fbd2","resolution":{"observed_at":"2026-08-12T11:20:17.345907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.324059Z","title":null,"venue":null,"work_id":"41173a5c-2db1-4e2a-9400-cb498216a269","year":2015},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.478528Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:02695734bf4000215e2bc5d06408fdec958c42c1d335ea36496d7d5e3dfaa1f1","observation_id":"51140472-94ba-4331-a801-991e547ba59f","resolution":{"observed_at":"2026-08-12T11:20:17.329399Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.305647Z","title":null,"venue":null,"work_id":"f62a5f3d-18d0-4eba-8a96-cb23ac85a69c","year":null},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.483676Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:f612f0a86cf5f98d700747a23fd3e0bdc0c48702f71df1f06a061ce344a192a5","observation_id":"135863f0-f724-4ceb-9506-e21ca4e4ec9c","resolution":{"observed_at":"2026-08-12T11:20:17.311079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.488994Z","title":"On human motion prediction using recurrent neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.488994Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:889a726b182c072acffc1762e25f265499e9473f7e887e966898b06c2fd99f01","observation_id":"a7d3a749-3b9d-403e-bbf6-87f0882121c0","resolution":{"observed_at":"2026-08-12T11:20:16.488994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.276421Z","title":"Create more natural movements using inside-out body tracking and generative legs, 2023","venue":null,"work_id":"7d405877-6d83-4743-85d8-01331db41f9f","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.493903Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:9ee79efb016bbf6707263a88f76449f66521502f189740c7dd5113d0593bacf1","observation_id":"8766b07e-35a2-448b-9c06-1db290d88072","resolution":{"observed_at":"2026-08-12T11:20:17.281992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.256833Z","title":"You2me: Inferring body pose in egocentric video via first and second person interactions","venue":null,"work_id":"aa653d32-be00-4e23-b361-a9c91888a9a4","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.499993Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:c28f92171af6d91563a2c2f7bdbe2cca3eb85d880a48d862af7856db0108a4a1","observation_id":"0d8c9266-60ac-44b5-84d0-1449e476c4b8","resolution":{"observed_at":"2026-08-12T11:20:17.262596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.240100Z","title":"Punnakkal, Arjun Chandrasekaran, Nikos Athanasiou, Alejandra Quiros-Ramirez, and Michael J","venue":null,"work_id":"3cf38f22-9d58-422f-afe9-53e945257993","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.510945Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:68bbb9bcecf8eaa0c2508e468b796407958212715495b94dd477f60cefee7cba","observation_id":"2953a302-149a-4fdc-9662-aafc0b699c91","resolution":{"observed_at":"2026-08-12T11:20:17.245435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.517126Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.517126Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:7fcfb524002f2ef5b683f18beba88d8220ce1344d47336c359f95636e32a65ef","observation_id":"08294538-8497-46d3-9aae-18cc3da2ec78","resolution":{"observed_at":"2026-08-12T11:20:16.517126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02413","last_updated":"2017-06-07T23:37:44Z","snapshot_observed_at":"2026-07-06T05:46:01.107746Z","submitted_at":"2017-06-07T23:37:44Z","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02413","snapshot_observed_at":"2026-08-12T11:20:16.522054Z","title":"Point- net++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.522054Z"},"links":{"cited_paper":"/paper/1706.02413","citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:5894821c7adb1eb91a35439c1e66582a6ae357eeec346ff4f6982f7da063b7df","observation_id":"44ecb2c7-3dd3-4e41-bc66-28a870ec575a","resolution":{"observed_at":"2026-08-12T11:20:16.522054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.211110Z","title":"Pointnext: Revisiting pointnet++ with improved training and scaling strategies","venue":null,"work_id":"086f90a5-f0d2-42c7-9ff9-258ad4c7e563","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.527532Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:a4ad80320a66d0dbcbd4704222f65b3d78495be933cf1aabe359138799dddc38","observation_id":"e2461ca5-2fa4-45a2-a720-4c245ff5c5a4","resolution":{"observed_at":"2026-08-12T11:20:17.217601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.194695Z","title":null,"venue":null,"work_id":"c71b0d37-e07a-4afa-bead-305e55347c64","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.532857Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:c87bd9b8f268b88dbc8f9ef3f0074b9cde2116761bf05523688e433bb9d735d7","observation_id":"196befb1-e1d4-4c84-ac39-bdc16e03a4a1","resolution":{"observed_at":"2026-08-12T11:20:17.200370Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.176343Z","title":"Livehps: Lidar-based scene- level human pose and shape estimation in free environment","venue":null,"work_id":"570b5160-5880-4f9b-9dc2-4e7071a93b29","year":2024},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.538473Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:3c85f19190c2d0587b1c81da7e92252d41a16b921393bd890f86f99881a7aeba","observation_id":"7e362432-1c2c-4b68-a55c-5b104a866b51","resolution":{"observed_at":"2026-08-12T11:20:17.182943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.158559Z","title":"Lidar-aid inertial poser: Large-scale human motion capture by sparse inertial and lidar sensors","venue":null,"work_id":"ff5e6d0e-e53f-4f11-928a-d514eb062ffc","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.543582Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:46a016a2b8b7bf553fed661e46484df0e4a36087ff9b9a6e3be9e3909a485b6c","observation_id":"e7bfec62-10c7-4bf0-9b03-fb947d4f027d","resolution":{"observed_at":"2026-08-12T11:20:17.164393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.140196Z","title":"Egocap: egocentric marker-less mo- tion capture with two fisheye cameras","venue":null,"work_id":"47ebc0e7-ea21-4a9d-b763-58da93c37889","year":2016},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.550732Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:bb2a62af1cd90fbae5e8db30dacf4eccf5a8b429cc5bd870436131711b84fbd6","observation_id":"4920b25b-19ef-4a81-b08e-dfc9af3c86ba","resolution":{"observed_at":"2026-08-12T11:20:17.146573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.123914Z","title":"Real-time 3d pose estimation from sin- gle depth images","venue":null,"work_id":"e5ecd279-9e79-41c5-9adc-a9829dd5513d","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.556388Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:29e9fd9c0acbad36989ae373e0d1d27fd44f70df5a305e59ddc430ae4c519556","observation_id":"2f4808a4-3b89-4b61-9976-03d3cc78930e","resolution":{"observed_at":"2026-08-12T11:20:17.129142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.105912Z","title":"Phasemp: Robust 3d pose estimation via phase-conditioned human motion prior","venue":null,"work_id":"9e50010b-7131-4b3a-b1d0-15077c7342ba","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.561358Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:1b9cf4cbeae0be44dc92b2138e837c4d3e48569d0397ede9723a9b0b9e39dbcb","observation_id":"4024282f-c67f-445c-92f3-574ded0e7d24","resolution":{"observed_at":"2026-08-12T11:20:17.111827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14916","last_updated":"2022-10-03T09:17:41Z","snapshot_observed_at":"2026-08-12T18:48:37.916493Z","submitted_at":"2022-09-29T16:27:53Z","title":"Human Motion Diffusion Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14916","snapshot_observed_at":"2026-08-12T11:20:16.566517Z","title":"Human motion dif- fusion model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.566517Z"},"links":{"cited_paper":"/paper/2209.14916","citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:5632afe0c502273006d71a385ea2fa8e3b7f79e96b9441adb3ade9af1eb066fa","observation_id":"4739416a-a014-432c-a0f9-2ee1e91fe7b5","resolution":{"observed_at":"2026-08-12T11:20:16.566517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.083032Z","title":null,"venue":null,"work_id":"812dbbb4-27e3-4502-b316-a74ffee24867","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.572302Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:13a52562b9bfb215119c21ec887677dd0248f976d9c2cf9ced5bf3afc7c2dfea","observation_id":"e1b85d17-06bf-40a7-84f3-091ea96b17b4","resolution":{"observed_at":"2026-08-12T11:20:17.088901Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.063901Z","title":"xr-egopose: Egocentric 3d human pose from an hmd camera","venue":null,"work_id":"adb16960-5401-4a92-acb6-395f4ef2e96b","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.577999Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:88ccddffa4d168a39290e63f6f4ff68038a0c32c5dae3ee9b11ce3ce4a24c6f8","observation_id":"94716890-9038-4406-9193-aa3020cbc1dc","resolution":{"observed_at":"2026-08-12T11:20:17.070083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.040344Z","title":"Multi-person 3d pose estimation from 3d cloud data using 3d convolutional neural networks","venue":null,"work_id":"1bc79a63-8a8c-4d84-8ea0-5c2dfe8cc05b","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.582689Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:9ae6df84f4d7781eae6999afc13581c3b67888cbe0a3a6d16296ec021d215d6b","observation_id":"a8bf2f56-1bc1-4bb2-8650-8120b01bc067","resolution":{"observed_at":"2026-08-12T11:20:17.050710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:17.020719Z","title":"Egocentric whole-body motion capture with fisheyevit and diffusion-based motion refinement","venue":null,"work_id":"2a12b012-5c94-4cb4-bcdd-4e77d47b9e33","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.587205Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:3f4989c5e9cb730812cc6a4331727497ebdb5aa38b993aee44552e1548eb449a","observation_id":"2e63cb00-ad4f-477c-81b8-afed043b4281","resolution":{"observed_at":"2026-08-12T11:20:17.026491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.999773Z","title":"Sequential 3d human pose and shape estimation from point clouds","venue":null,"work_id":"c0d279a8-086d-4504-935e-fb03ab25b925","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.592312Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:6c3c73ca4790c223770e338b382a34211eeee176a0b14236f270d8b10ad007f8","observation_id":"070b5f3e-20ed-4007-a879-f4328ab9004d","resolution":{"observed_at":"2026-08-12T11:20:17.008063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.980409Z","title":"Parametric model estimation for 3d clothed humans from point clouds","venue":null,"work_id":"1e173e27-45b1-4c2c-828d-f82940ab9a87","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.597686Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:2e1e1b08c12e4cd884029b649c7289aef80148fd7af97a55174137b8e250a45f","observation_id":"03014204-c95b-424c-bd1b-62104b9e63dd","resolution":{"observed_at":"2026-08-12T11:20:16.986599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.963050Z","title":"Gorban, Jingwei Ji, Mahyar Najibi, Yin Zhou, and Dragomir Anguelov","venue":null,"work_id":"e8557e98-115e-4e65-b454-876243250996","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.602883Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:0687f061fe688d4c1c6862a51b144322b3651500aa6b38c3b626b7648cd9fadc","observation_id":"ab6d5e0e-5a0a-488a-ae61-eb9b88448a0f","resolution":{"observed_at":"2026-08-12T11:20:16.968572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.946740Z","title":"Quest- sim: Human motion tracking from sparse sensors with sim- ulated avatars","venue":null,"work_id":"79aed16e-9644-4d19-899e-3d3a9fa14e33","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.608069Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:563f83da33757bbdbd5f1187d7b89fc5a6cc574368add02b034ac68143baa2fd","observation_id":"20575f35-28d7-4c00-a4fa-b2ad5db3ef69","resolution":{"observed_at":"2026-08-12T11:20:16.952282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.929602Z","title":"Neuralpassthrough: Learned real-time view synthesis for vr","venue":null,"work_id":"e6c54bdb-b5c5-4365-b58f-2fba27d25e99","year":2022},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.612860Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:99041b5bd4934c4f64969f8952d85bc4a4543d8c6900979190bd3729f14407b6","observation_id":"fb24a7a0-0f22-471e-aca1-53d2938dfa93","resolution":{"observed_at":"2026-08-12T11:20:16.935180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.912252Z","title":"A re- view: Point cloud-based 3d human joints estimation","venue":null,"work_id":"504e30e3-a898-4394-a6f4-9869c826319d","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.617974Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:b3c2519ed181b4571225132fc340aa5f648c13883852dab2a67666c0a7a2b212","observation_id":"5682578c-3975-4350-a084-98e252b08b9d","resolution":{"observed_at":"2026-08-12T11:20:16.917896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.894851Z","title":"Lobstr: Real-time lower-body pose prediction from sparse upper- body tracking signals","venue":null,"work_id":"1d028d9d-cedc-4964-b98c-027559204271","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.623895Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:053655eba4af3b84a75b342e1055855190bdaad1c4cd5302139d70bff6e5862c","observation_id":"42258c3b-ce7d-42ab-9c63-b86267a71834","resolution":{"observed_at":"2026-08-12T11:20:16.900044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.877437Z","title":"Neu- ral3points: Learning to generate physically realistic full- body motion for virtual reality users","venue":null,"work_id":"4ec5c256-13d2-47fb-b4b1-a305e61f2fe7","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.628911Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:32d0fe0a00a5ef49b0f7a042c841992ebe94d1af34f4e5316dd94e4c8304d7bb","observation_id":"6f91105c-a72b-42c7-b3f4-6b1a6e674030","resolution":{"observed_at":"2026-08-12T11:20:16.883082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.860213Z","title":"3d ego-pose estimation via imita- tion learning","venue":null,"work_id":"7e071577-f915-403e-b94c-cb91f1a02e32","year":2018},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.634120Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:0efa29b1812a94312d2fbe4ec391a8aa10bb7e3918b30858e00272e8e9ceed02","observation_id":"a2cdc635-c0c5-490c-95dc-7ff8add9d91b","resolution":{"observed_at":"2026-08-12T11:20:16.865490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.841444Z","title":"Physdiff: Physics-guided human motion diffusion model","venue":null,"work_id":"6c3d20bc-588d-4c3e-807f-344e304ecf0f","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.640246Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:d603771836c02bc77db9191070768dea87cbf86bd96b45c23b71544dd0ae465a","observation_id":"1f17cc76-9896-43fc-8caf-d5fbe42b72f7","resolution":{"observed_at":"2026-08-12T11:20:16.847935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.824182Z","title":"Motiondif- fuse: Text-driven human motion generation with diffusion model","venue":null,"work_id":"ec9d782b-5fdb-4c31-824d-c46025fd0c59","year":2024},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.649525Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:3ac715d2fb7def2c3589534469fd41d811c7044498adb0372d6b573d166c02eb","observation_id":"1b616cc9-9809-49f6-965a-aa9e2cf5ebf6","resolution":{"observed_at":"2026-08-12T11:20:16.829365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.808278Z","title":"Weakly supervised adversarial learning for 3d human pose estimation from point clouds","venue":null,"work_id":"f058a7cc-25bf-4dd2-86c1-3bacb5652a84","year":2020},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.654833Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:12cffccd565848e0c1d3e9d5a2e3ffebdec659086d3412ab73c637035aa29ebe","observation_id":"752565e6-c1c0-44a6-910a-3fc5741d2318","resolution":{"observed_at":"2026-08-12T11:20:16.813464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.791608Z","title":"Egoglass: Egocentric-view human pose estimation from an eyeglass frame","venue":null,"work_id":"c5c4a758-7c35-4336-98ba-57da64383b76","year":2021},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.660203Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:ef229059fee24b359ac930022623be6ce557cd5c8858804d5efaedd80f2b6313","observation_id":"5298ab43-4f12-43ae-abb9-45d43ff0d834","resolution":{"observed_at":"2026-08-12T11:20:16.797004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.772910Z","title":"Realistic full-body tracking from sparse observa- tions via joint-level modeling","venue":null,"work_id":"afcfb8d7-3a18-402e-84bc-1224b5aafb9f","year":2023},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.666365Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:3a76248a31b7277490db7463f19cfd9b4d843cb78faa144b1320dcd56eeca16f","observation_id":"69ae9459-885e-469d-af6d-2b7a41a01f52","resolution":{"observed_at":"2026-08-12T11:20:16.779219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:20:16.752082Z","title":"On the continuity of rotation representations in neural networks","venue":null,"work_id":"258df6b3-2491-43b5-b261-75e4d5c97c9a","year":2019},"citing_paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T11:20:16.672395Z"},"links":{"citing_paper":"/paper/2411.18377"},"observation_digest":"sha256:c1e6e255f07bbf39629805944b5f56502a5d29eb0663b97021955382fe9c7544","observation_id":"eca5eff2-ff0e-4af2-9035-cf40c9e124df","resolution":{"observed_at":"2026-08-12T11:20:16.761040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.18377","last_updated":"2024-11-27T14:25:32Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T11:13:56.771798Z","submitted_at":"2024-11-27T14:25:32Z","title":"XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":46},"total_outbound_references":58},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2411.18377."}