{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:TCM4TXRD3Q63BRVRPCO6X6PEMR","short_pith_number":"pith:TCM4TXRD","canonical_record":{"source":{"id":"2304.04956","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T03:59:43Z","cross_cats_sorted":[],"title_canon_sha256":"5b16d9cd57064f5f747ebaeb4638bcbd18e07a350a1918e515190fc818b92173","abstract_canon_sha256":"8ead626043ce7a4203c95113143c5fa171014aa0410d511b7937ac5419458c1d"},"schema_version":"1.0"},"canonical_sha256":"9899c9de23dc3db0c6b1789debf9e4646be0dffac0757e97f8bc7ddb5358a939","source":{"kind":"arxiv","id":"2304.04956","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.04956","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"arxiv_version","alias_value":"2304.04956v1","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.04956","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_12","alias_value":"TCM4TXRD3Q63","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_16","alias_value":"TCM4TXRD3Q63BRVR","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_8","alias_value":"TCM4TXRD","created_at":"2026-07-05T05:59:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:TCM4TXRD3Q63BRVRPCO6X6PEMR","target":"record","payload":{"canonical_record":{"source":{"id":"2304.04956","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T03:59:43Z","cross_cats_sorted":[],"title_canon_sha256":"5b16d9cd57064f5f747ebaeb4638bcbd18e07a350a1918e515190fc818b92173","abstract_canon_sha256":"8ead626043ce7a4203c95113143c5fa171014aa0410d511b7937ac5419458c1d"},"schema_version":"1.0"},"canonical_sha256":"9899c9de23dc3db0c6b1789debf9e4646be0dffac0757e97f8bc7ddb5358a939","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:59:53.518579Z","signature_b64":"Zp360IJCld/xEYzwPI3iyjaL6hsrfQt+zl3cgcc9JbtlX+mRJm52WZoM1Q0LLY4QVBYIVBIjjNMD8SVYOJ0GCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9899c9de23dc3db0c6b1789debf9e4646be0dffac0757e97f8bc7ddb5358a939","last_reissued_at":"2026-07-05T05:59:53.518093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:59:53.518093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.04956","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:59:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ifwg855TvNoeIaKro+T/VHs6TFwr/Ky6EDC7hcqyf/SHYTZFRDi4FOW/m4Qo0fwTRgmxVOcDF1S7FSJ1y3N8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:05:45.758938Z"},"content_sha256":"251959897ee8c4a98a15b2219cbf895cb42352a6f3840a5eea86d137f48c12a9","schema_version":"1.0","event_id":"sha256:251959897ee8c4a98a15b2219cbf895cb42352a6f3840a5eea86d137f48c12a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:TCM4TXRD3Q63BRVRPCO6X6PEMR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Graph Convolution Network for Pose Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongwei Ren, Kewei Liang, Yuhong Shi","submitted_at":"2023-04-11T03:59:43Z","abstract_excerpt":"Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.04956","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.04956/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:59:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b68pI88vAwym680vkCTpITD7Dyp9297P01bABsViqfpz0qa0oaz4rHQ31K13Fy1Kp2wkffyKo8wHvNCM4QyrBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:05:45.759412Z"},"content_sha256":"d5b2cc72635bbfd75d11a4b3019dabd0f49a592cc958b8359eada302be0137e1","schema_version":"1.0","event_id":"sha256:d5b2cc72635bbfd75d11a4b3019dabd0f49a592cc958b8359eada302be0137e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/bundle.json","state_url":"https://pith.science/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T09:05:45Z","links":{"resolver":"https://pith.science/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR","bundle":"https://pith.science/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/bundle.json","state":"https://pith.science/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TCM4TXRD3Q63BRVRPCO6X6PEMR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TCM4TXRD3Q63BRVRPCO6X6PEMR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8ead626043ce7a4203c95113143c5fa171014aa0410d511b7937ac5419458c1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T03:59:43Z","title_canon_sha256":"5b16d9cd57064f5f747ebaeb4638bcbd18e07a350a1918e515190fc818b92173"},"schema_version":"1.0","source":{"id":"2304.04956","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.04956","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"arxiv_version","alias_value":"2304.04956v1","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.04956","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_12","alias_value":"TCM4TXRD3Q63","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_16","alias_value":"TCM4TXRD3Q63BRVR","created_at":"2026-07-05T05:59:53Z"},{"alias_kind":"pith_short_8","alias_value":"TCM4TXRD","created_at":"2026-07-05T05:59:53Z"}],"graph_snapshots":[{"event_id":"sha256:d5b2cc72635bbfd75d11a4b3019dabd0f49a592cc958b8359eada302be0137e1","target":"graph","created_at":"2026-07-05T05:59:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2304.04956/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) an","authors_text":"Hongwei Ren, Kewei Liang, Yuhong Shi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T03:59:43Z","title":"Multi-Graph Convolution Network for Pose Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.04956","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:251959897ee8c4a98a15b2219cbf895cb42352a6f3840a5eea86d137f48c12a9","target":"record","created_at":"2026-07-05T05:59:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8ead626043ce7a4203c95113143c5fa171014aa0410d511b7937ac5419458c1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T03:59:43Z","title_canon_sha256":"5b16d9cd57064f5f747ebaeb4638bcbd18e07a350a1918e515190fc818b92173"},"schema_version":"1.0","source":{"id":"2304.04956","kind":"arxiv","version":1}},"canonical_sha256":"9899c9de23dc3db0c6b1789debf9e4646be0dffac0757e97f8bc7ddb5358a939","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9899c9de23dc3db0c6b1789debf9e4646be0dffac0757e97f8bc7ddb5358a939","first_computed_at":"2026-07-05T05:59:53.518093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:59:53.518093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zp360IJCld/xEYzwPI3iyjaL6hsrfQt+zl3cgcc9JbtlX+mRJm52WZoM1Q0LLY4QVBYIVBIjjNMD8SVYOJ0GCg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:59:53.518579Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.04956","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:251959897ee8c4a98a15b2219cbf895cb42352a6f3840a5eea86d137f48c12a9","sha256:d5b2cc72635bbfd75d11a4b3019dabd0f49a592cc958b8359eada302be0137e1"],"state_sha256":"930412a5f4ad454c3a3589694c233fffc15030a38076bed65d01b255c7ab6457"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXxhtypkpcguygVzFxjh8Hs4mlytulqoCKqy7R/mEUm9x36u6I7qey54mWgw8X2gxVA3cWbMzPcSyKm1hyOrCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T09:05:45.762577Z","bundle_sha256":"7ab34593661b1e3e327153667c20a63b1c32457306aebb9158ee87ea6c67c314"}}