{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GHINEWB577NFPVAPPJURO5DZPQ","short_pith_number":"pith:GHINEWB5","canonical_record":{"source":{"id":"2302.04774","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-09T17:08:43Z","cross_cats_sorted":[],"title_canon_sha256":"066a630726ac94cfe4e6ac256dedf437bef4ca901ad94b2a70d915e2abc03cfe","abstract_canon_sha256":"ba1228ce1f870c067a8eaa4bddab0db89e86464ba69aa96a511de9154a5f6479"},"schema_version":"1.0"},"canonical_sha256":"31d0d2583dffda57d40f7a691774797c0ebd675f21d7554f8e8004c57d28af39","source":{"kind":"arxiv","id":"2302.04774","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04774","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04774v4","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04774","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"GHINEWB577NF","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_16","alias_value":"GHINEWB577NFPVAP","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_8","alias_value":"GHINEWB5","created_at":"2026-07-05T06:03:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GHINEWB577NFPVAPPJURO5DZPQ","target":"record","payload":{"canonical_record":{"source":{"id":"2302.04774","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-09T17:08:43Z","cross_cats_sorted":[],"title_canon_sha256":"066a630726ac94cfe4e6ac256dedf437bef4ca901ad94b2a70d915e2abc03cfe","abstract_canon_sha256":"ba1228ce1f870c067a8eaa4bddab0db89e86464ba69aa96a511de9154a5f6479"},"schema_version":"1.0"},"canonical_sha256":"31d0d2583dffda57d40f7a691774797c0ebd675f21d7554f8e8004c57d28af39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:03:27.648042Z","signature_b64":"4Lmiqkn3IOIFnwtuH60/aV23LrYyTYeHCfP/muszVD7SIgvh1IOb03mWBNfE7746Mtk9MqQ7K8HOWSZRFbivAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31d0d2583dffda57d40f7a691774797c0ebd675f21d7554f8e8004c57d28af39","last_reissued_at":"2026-07-05T06:03:27.647712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:03:27.647712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.04774","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZ3+jlSobizoujU8AUXZJiv1ZVpXT1nUdbks76e6kEmC/c/t2D/fRX4jz+y32++ODRUzbDXSyrTc7DmmUxRtCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T05:22:30.020619Z"},"content_sha256":"c16077ed44a96501bf6afe95bf1ef1d4fa2741c86c90b2f21bcc3176dee75e27","schema_version":"1.0","event_id":"sha256:c16077ed44a96501bf6afe95bf1ef1d4fa2741c86c90b2f21bcc3176dee75e27"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GHINEWB577NFPVAPPJURO5DZPQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D Human Pose and Shape Estimation via HybrIK-Transformer","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Boris N. Oreshkin","submitted_at":"2023-02-09T17:08:43Z","abstract_excerpt":"HybrIK relies on a combination of analytical inverse kinematics and deep learning to produce more accurate 3D pose estimation from 2D monocular images. HybrIK has three major components: (1) pretrained convolution backbone, (2) deconvolution to lift 3D pose from 2D convolution features, (3) analytical inverse kinematics pass correcting deep learning prediction using learned distribution of plausible twist and swing angles. In this paper we propose an enhancement of the 2D to 3D lifting module, replacing deconvolution with Transformer, resulting in accuracy and computational efficiency improvem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04774","kind":"arxiv","version":4},"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/2302.04774/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":"2026-07-05T06:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KK4P6+x+FNWSFin1TsYYdAxYFst9sz2c0VOsX9mJbPrSCQ9req9F5qG4no6T63J3pD5TERL1gmPBFuHNALdjAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T05:22:30.020980Z"},"content_sha256":"5da37533909303c3419194e78b6626c3af38a72010ba6512376ebec526548a08","schema_version":"1.0","event_id":"sha256:5da37533909303c3419194e78b6626c3af38a72010ba6512376ebec526548a08"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GHINEWB577NFPVAPPJURO5DZPQ/bundle.json","state_url":"https://pith.science/pith/GHINEWB577NFPVAPPJURO5DZPQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GHINEWB577NFPVAPPJURO5DZPQ/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":"2026-07-10T05:22:30Z","links":{"resolver":"https://pith.science/pith/GHINEWB577NFPVAPPJURO5DZPQ","bundle":"https://pith.science/pith/GHINEWB577NFPVAPPJURO5DZPQ/bundle.json","state":"https://pith.science/pith/GHINEWB577NFPVAPPJURO5DZPQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GHINEWB577NFPVAPPJURO5DZPQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GHINEWB577NFPVAPPJURO5DZPQ","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":"ba1228ce1f870c067a8eaa4bddab0db89e86464ba69aa96a511de9154a5f6479","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-09T17:08:43Z","title_canon_sha256":"066a630726ac94cfe4e6ac256dedf437bef4ca901ad94b2a70d915e2abc03cfe"},"schema_version":"1.0","source":{"id":"2302.04774","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04774","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04774v4","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04774","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"GHINEWB577NF","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_16","alias_value":"GHINEWB577NFPVAP","created_at":"2026-07-05T06:03:27Z"},{"alias_kind":"pith_short_8","alias_value":"GHINEWB5","created_at":"2026-07-05T06:03:27Z"}],"graph_snapshots":[{"event_id":"sha256:5da37533909303c3419194e78b6626c3af38a72010ba6512376ebec526548a08","target":"graph","created_at":"2026-07-05T06:03:27Z","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/2302.04774/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"HybrIK relies on a combination of analytical inverse kinematics and deep learning to produce more accurate 3D pose estimation from 2D monocular images. HybrIK has three major components: (1) pretrained convolution backbone, (2) deconvolution to lift 3D pose from 2D convolution features, (3) analytical inverse kinematics pass correcting deep learning prediction using learned distribution of plausible twist and swing angles. In this paper we propose an enhancement of the 2D to 3D lifting module, replacing deconvolution with Transformer, resulting in accuracy and computational efficiency improvem","authors_text":"Boris N. Oreshkin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-09T17:08:43Z","title":"3D Human Pose and Shape Estimation via HybrIK-Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04774","kind":"arxiv","version":4},"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:c16077ed44a96501bf6afe95bf1ef1d4fa2741c86c90b2f21bcc3176dee75e27","target":"record","created_at":"2026-07-05T06:03:27Z","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":"ba1228ce1f870c067a8eaa4bddab0db89e86464ba69aa96a511de9154a5f6479","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-09T17:08:43Z","title_canon_sha256":"066a630726ac94cfe4e6ac256dedf437bef4ca901ad94b2a70d915e2abc03cfe"},"schema_version":"1.0","source":{"id":"2302.04774","kind":"arxiv","version":4}},"canonical_sha256":"31d0d2583dffda57d40f7a691774797c0ebd675f21d7554f8e8004c57d28af39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31d0d2583dffda57d40f7a691774797c0ebd675f21d7554f8e8004c57d28af39","first_computed_at":"2026-07-05T06:03:27.647712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:03:27.647712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Lmiqkn3IOIFnwtuH60/aV23LrYyTYeHCfP/muszVD7SIgvh1IOb03mWBNfE7746Mtk9MqQ7K8HOWSZRFbivAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:03:27.648042Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.04774","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c16077ed44a96501bf6afe95bf1ef1d4fa2741c86c90b2f21bcc3176dee75e27","sha256:5da37533909303c3419194e78b6626c3af38a72010ba6512376ebec526548a08"],"state_sha256":"7dedb815f98160419e05271310c69a8e0591cebae2e76c4350663b0c47046bb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"toXX2QRLxzY8A5KlQWNgRN1CnLo2sVXdTDogdvvW/k6Ts90K51aCNQrLNkbedvlsPXyD3VAjptcYHFy+NQgJBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T05:22:30.022852Z","bundle_sha256":"7749cd136f3617d7dcc675934caeb8eda5d52ebe6e00c1fbec70443ee6f506dd"}}