{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LTTSIVR2NCLHLFQZND2L5HGCRZ","short_pith_number":"pith:LTTSIVR2","schema_version":"1.0","canonical_sha256":"5ce724563a689675961968f4be9cc28e5ff56676a91881d4fa1c2b61195cf1f6","source":{"kind":"arxiv","id":"2412.20657","version":1},"attestation_state":"computed","paper":{"title":"Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cuixia Ma, Hongan Wang, Qiang He, Xiaoming Deng, Yanguang Wan, Yinda Zhang, Yonghao Zhang","submitted_at":"2024-12-30T02:21:43Z","abstract_excerpt":"Generating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics. The main challenge of this task lies in determining the level of involvement of each hand given the complex shapes of objects in different sizes and their different motion trajectories, while ensuring strong grasping realism and guaranteeing the coordination of movement in all body parts. Contrasting with existing work, which either generates human interaction motion sequences without detailed hand grasping poses or only model"},"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":"2412.20657","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-30T02:21:43Z","cross_cats_sorted":[],"title_canon_sha256":"91a5a2d8013c44f4b60fd955a85a768e33af6da2a6bd1a56989bbe9ea2d1761e","abstract_canon_sha256":"94513cce6cf5a101761e1408df75bdbe2586dfee88673b677fed489cdb34f234"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:20.423841Z","signature_b64":"ozHvN6iN8DH1vFZV22cUTPfMUauA8PkgHyhDP/xu8nrCRsre1E1ugmttQpw6UNPwohq1I//31j9OJmIRwAOfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ce724563a689675961968f4be9cc28e5ff56676a91881d4fa1c2b61195cf1f6","last_reissued_at":"2026-07-05T09:55:20.423194Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:20.423194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cuixia Ma, Hongan Wang, Qiang He, Xiaoming Deng, Yanguang Wan, Yinda Zhang, Yonghao Zhang","submitted_at":"2024-12-30T02:21:43Z","abstract_excerpt":"Generating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics. The main challenge of this task lies in determining the level of involvement of each hand given the complex shapes of objects in different sizes and their different motion trajectories, while ensuring strong grasping realism and guaranteeing the coordination of movement in all body parts. Contrasting with existing work, which either generates human interaction motion sequences without detailed hand grasping poses or only model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20657","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/2412.20657/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":"2412.20657","created_at":"2026-07-05T09:55:20.423271+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20657v1","created_at":"2026-07-05T09:55:20.423271+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20657","created_at":"2026-07-05T09:55:20.423271+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTTSIVR2NCLH","created_at":"2026-07-05T09:55:20.423271+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTTSIVR2NCLHLFQZ","created_at":"2026-07-05T09:55:20.423271+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTTSIVR2","created_at":"2026-07-05T09:55:20.423271+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13097","citing_title":"GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training","ref_index":73,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ","json":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ.json","graph_json":"https://pith.science/api/pith-number/LTTSIVR2NCLHLFQZND2L5HGCRZ/graph.json","events_json":"https://pith.science/api/pith-number/LTTSIVR2NCLHLFQZND2L5HGCRZ/events.json","paper":"https://pith.science/paper/LTTSIVR2"},"agent_actions":{"view_html":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ","download_json":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ.json","view_paper":"https://pith.science/paper/LTTSIVR2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20657&json=true","fetch_graph":"https://pith.science/api/pith-number/LTTSIVR2NCLHLFQZND2L5HGCRZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LTTSIVR2NCLHLFQZND2L5HGCRZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ/action/storage_attestation","attest_author":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ/action/author_attestation","sign_citation":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ/action/citation_signature","submit_replication":"https://pith.science/pith/LTTSIVR2NCLHLFQZND2L5HGCRZ/action/replication_record"}},"created_at":"2026-07-05T09:55:20.423271+00:00","updated_at":"2026-07-05T09:55:20.423271+00:00"}