{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4FUO5LCAZ45OTQRVRNTZ72XX77","short_pith_number":"pith:4FUO5LCA","schema_version":"1.0","canonical_sha256":"e168eeac40cf3ae9c2358b679feaf7ffd134a5a6035411ff675faeb8047dd444","source":{"kind":"arxiv","id":"2411.16758","version":4},"attestation_state":"computed","paper":{"title":"Motion-Aware Animatable Gaussian Avatars Deblurring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Muyao Niu, Qingtian Zhu, Wei Wang, Xiao Sun, Yifan Zhan, Yinqiang Zheng, Zhihang Zhong, Zhuoxiao Li","submitted_at":"2024-11-24T10:03:24Z","abstract_excerpt":"The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as input, which are often impractical to obtain in real-world scenarios due to variations in human motion speed and intensity. This paper introduces a novel method for directly reconstructing sharp 3D human Gaussian avatars from blurry videos. The proposed approach incorporates a 3D-aware, physics-based model of blur formation caused by human motion, together with a 3D human motion model designed to resolve ambiguities"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.16758","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-24T10:03:24Z","cross_cats_sorted":[],"title_canon_sha256":"9b4903606e95f797e68487684f2f8fe4ce70d207f7aebf298eab5867278e1e5b","abstract_canon_sha256":"e579c0e6c50bfbe4992d3ab3c135bb55c112f408997c7ca01695aac82cde994f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:19:53.887142Z","signature_b64":"T0vxIOBX+nv8KOTV1hf6zNfldNhccK9ehU+8saUuhd+rOe24tejQrjBj8RFwnSmOUT+P3YSb+qAgVcDy3uGtCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e168eeac40cf3ae9c2358b679feaf7ffd134a5a6035411ff675faeb8047dd444","last_reissued_at":"2026-08-11T01:19:53.884595Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:19:53.884595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion-Aware Animatable Gaussian Avatars Deblurring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Muyao Niu, Qingtian Zhu, Wei Wang, Xiao Sun, Yifan Zhan, Yinqiang Zheng, Zhihang Zhong, Zhuoxiao Li","submitted_at":"2024-11-24T10:03:24Z","abstract_excerpt":"The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as input, which are often impractical to obtain in real-world scenarios due to variations in human motion speed and intensity. This paper introduces a novel method for directly reconstructing sharp 3D human Gaussian avatars from blurry videos. The proposed approach incorporates a 3D-aware, physics-based model of blur formation caused by human motion, together with a 3D human motion model designed to resolve ambiguities"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16758","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/2411.16758/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.16758","created_at":"2026-08-11T01:19:53.885316+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16758v4","created_at":"2026-08-11T01:19:53.885316+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16758","created_at":"2026-08-11T01:19:53.885316+00:00"},{"alias_kind":"pith_short_12","alias_value":"4FUO5LCAZ45O","created_at":"2026-08-11T01:19:53.885316+00:00"},{"alias_kind":"pith_short_16","alias_value":"4FUO5LCAZ45OTQRV","created_at":"2026-08-11T01:19:53.885316+00:00"},{"alias_kind":"pith_short_8","alias_value":"4FUO5LCA","created_at":"2026-08-11T01:19:53.885316+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.16768","citing_title":"Sequential Gaussian Avatars with Hierarchical Motion Context","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77","json":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77.json","graph_json":"https://pith.science/api/pith-number/4FUO5LCAZ45OTQRVRNTZ72XX77/graph.json","events_json":"https://pith.science/api/pith-number/4FUO5LCAZ45OTQRVRNTZ72XX77/events.json","paper":"https://pith.science/paper/4FUO5LCA"},"agent_actions":{"view_html":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77","download_json":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77.json","view_paper":"https://pith.science/paper/4FUO5LCA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16758&json=true","fetch_graph":"https://pith.science/api/pith-number/4FUO5LCAZ45OTQRVRNTZ72XX77/graph.json","fetch_events":"https://pith.science/api/pith-number/4FUO5LCAZ45OTQRVRNTZ72XX77/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77/action/storage_attestation","attest_author":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77/action/author_attestation","sign_citation":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77/action/citation_signature","submit_replication":"https://pith.science/pith/4FUO5LCAZ45OTQRVRNTZ72XX77/action/replication_record"}},"created_at":"2026-08-11T01:19:53.885316+00:00","updated_at":"2026-08-11T01:19:53.885316+00:00"}