{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:43WLPNQA2NUYUOBEDMGKCRLRJN","short_pith_number":"pith:43WLPNQA","canonical_record":{"source":{"id":"2412.10458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-12T08:27:15Z","cross_cats_sorted":["cs.GR","cs.HC"],"title_canon_sha256":"b543e0071daeb26f2c98d5a4740884a908afc0805e3cf11df60a9cd22316be42","abstract_canon_sha256":"b4a3b7cd25e0d055dd66c5d34be1d7aa641d8b2cc45b92188fae44584360a2e5"},"schema_version":"1.0"},"canonical_sha256":"e6ecb7b600d3698a38241b0ca145714b5c76894dba703561b982dfebeb4b7c2c","source":{"kind":"arxiv","id":"2412.10458","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.10458","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"arxiv_version","alias_value":"2412.10458v1","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10458","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_12","alias_value":"43WLPNQA2NUY","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_16","alias_value":"43WLPNQA2NUYUOBE","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_8","alias_value":"43WLPNQA","created_at":"2026-07-05T09:49:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:43WLPNQA2NUYUOBEDMGKCRLRJN","target":"record","payload":{"canonical_record":{"source":{"id":"2412.10458","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-12T08:27:15Z","cross_cats_sorted":["cs.GR","cs.HC"],"title_canon_sha256":"b543e0071daeb26f2c98d5a4740884a908afc0805e3cf11df60a9cd22316be42","abstract_canon_sha256":"b4a3b7cd25e0d055dd66c5d34be1d7aa641d8b2cc45b92188fae44584360a2e5"},"schema_version":"1.0"},"canonical_sha256":"e6ecb7b600d3698a38241b0ca145714b5c76894dba703561b982dfebeb4b7c2c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:16.268614Z","signature_b64":"eCmnp5c1B1xAywqKrrWB3KKnJnsZrhszbeGjRA0V0rmvY0EtcroBOsZlJZM5Izs+FhDjmaHvJ1iSd4oUEWMxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6ecb7b600d3698a38241b0ca145714b5c76894dba703561b982dfebeb4b7c2c","last_reissued_at":"2026-07-05T09:49:16.268175Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:16.268175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.10458","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":"2026-07-05T09:49:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9R4Dw8ZkJW7aS7Isibm5jYlfMTds19d2cDQqqj2ogBtBMvWYU1xyZNhFSwb/2sKdxsLVTYCWJuUgR83rFPK/Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T18:04:10.418642Z"},"content_sha256":"9e7f440577557575b8a27eb3fc266b906e33e5eade9d6fb723e6bf570983f4fb","schema_version":"1.0","event_id":"sha256:9e7f440577557575b8a27eb3fc266b906e33e5eade9d6fb723e6bf570983f4fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:43WLPNQA2NUYUOBEDMGKCRLRJN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.HC"],"primary_cat":"cs.CV","authors_text":"Dongdong Weng, Jiayi Zhao, Qiuxin Du, Zeyu Tian","submitted_at":"2024-12-12T08:27:15Z","abstract_excerpt":"Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspace"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10458","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/2412.10458/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":"2026-07-05T09:49:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mlE993n0/CQNkmt9Xk4zd+xnkrE0vdxfuFBnY46Spl2KWk427IoUVwhlUroUZT1Cen6avOYKJ8dzjRBcJZBCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T18:04:10.419177Z"},"content_sha256":"31589ed6bc0f5af57af064d523ef5709d965f55bdd6ff011ecf16fc994fac959","schema_version":"1.0","event_id":"sha256:31589ed6bc0f5af57af064d523ef5709d965f55bdd6ff011ecf16fc994fac959"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/bundle.json","state_url":"https://pith.science/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/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":"2026-08-12T18:04:10Z","links":{"resolver":"https://pith.science/pith/43WLPNQA2NUYUOBEDMGKCRLRJN","bundle":"https://pith.science/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/bundle.json","state":"https://pith.science/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/43WLPNQA2NUYUOBEDMGKCRLRJN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:43WLPNQA2NUYUOBEDMGKCRLRJN","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":"b4a3b7cd25e0d055dd66c5d34be1d7aa641d8b2cc45b92188fae44584360a2e5","cross_cats_sorted":["cs.GR","cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-12T08:27:15Z","title_canon_sha256":"b543e0071daeb26f2c98d5a4740884a908afc0805e3cf11df60a9cd22316be42"},"schema_version":"1.0","source":{"id":"2412.10458","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.10458","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"arxiv_version","alias_value":"2412.10458v1","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10458","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_12","alias_value":"43WLPNQA2NUY","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_16","alias_value":"43WLPNQA2NUYUOBE","created_at":"2026-07-05T09:49:16Z"},{"alias_kind":"pith_short_8","alias_value":"43WLPNQA","created_at":"2026-07-05T09:49:16Z"}],"graph_snapshots":[{"event_id":"sha256:31589ed6bc0f5af57af064d523ef5709d965f55bdd6ff011ecf16fc994fac959","target":"graph","created_at":"2026-07-05T09:49:16Z","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/2412.10458/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspace","authors_text":"Dongdong Weng, Jiayi Zhao, Qiuxin Du, Zeyu Tian","cross_cats":["cs.GR","cs.HC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-12T08:27:15Z","title":"Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10458","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":"sha256:9e7f440577557575b8a27eb3fc266b906e33e5eade9d6fb723e6bf570983f4fb","target":"record","created_at":"2026-07-05T09:49:16Z","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":"b4a3b7cd25e0d055dd66c5d34be1d7aa641d8b2cc45b92188fae44584360a2e5","cross_cats_sorted":["cs.GR","cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-12T08:27:15Z","title_canon_sha256":"b543e0071daeb26f2c98d5a4740884a908afc0805e3cf11df60a9cd22316be42"},"schema_version":"1.0","source":{"id":"2412.10458","kind":"arxiv","version":1}},"canonical_sha256":"e6ecb7b600d3698a38241b0ca145714b5c76894dba703561b982dfebeb4b7c2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6ecb7b600d3698a38241b0ca145714b5c76894dba703561b982dfebeb4b7c2c","first_computed_at":"2026-07-05T09:49:16.268175Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:49:16.268175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eCmnp5c1B1xAywqKrrWB3KKnJnsZrhszbeGjRA0V0rmvY0EtcroBOsZlJZM5Izs+FhDjmaHvJ1iSd4oUEWMxAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:49:16.268614Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.10458","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9e7f440577557575b8a27eb3fc266b906e33e5eade9d6fb723e6bf570983f4fb","sha256:31589ed6bc0f5af57af064d523ef5709d965f55bdd6ff011ecf16fc994fac959"],"state_sha256":"e5a162881880b7b3922d353e8db09ca6658587ea33ebd7531a9afca975174dfd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rd5lbikoRdw2hluQ+cSgz/g6Q2z6GtylTkVU+dx0meq5Q1CCKrJToPyrt1obQWCuK8IIGDD2MInmsOGi4pH9Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T18:04:10.423777Z","bundle_sha256":"0e497090764b401dcb72f0deb94a78660fbe7150befbd4c89c5ba8bd8b4cdabb"}}