{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TKTLAHG4QTA2ZAGOJHPLO3HPBC","short_pith_number":"pith:TKTLAHG4","schema_version":"1.0","canonical_sha256":"9aa6b01cdc84c1ac80ce49deb76cef08ad9418f38b171dc1c0827b4e558542c1","source":{"kind":"arxiv","id":"2409.07179","version":1},"attestation_state":"computed","paper":{"title":"Phy124: Fast Physics-Driven 4D Content Generation from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiajing Lin, Min Jiang, Yongjie Hou, Yuzhou Tang, Zhenzhong Wang","submitted_at":"2024-09-11T10:41:46Z","abstract_excerpt":"4D content generation focuses on creating dynamic 3D objects that change over time. Existing methods primarily rely on pre-trained video diffusion models, utilizing sampling processes or reference videos. However, these approaches face significant challenges. Firstly, the generated 4D content often fails to adhere to real-world physics since video diffusion models do not incorporate physical priors. Secondly, the extensive sampling process and the large number of parameters in diffusion models result in exceedingly time-consuming generation processes. To address these issues, we introduce Phy1"},"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":"2409.07179","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T10:41:46Z","cross_cats_sorted":[],"title_canon_sha256":"92330d866afd3a5c6668b49b86b1a67b8f76906ef24e77fdfa89ba5c45fe1bc1","abstract_canon_sha256":"e33b2047af820881e603a20e942988225d6f45c65f2cd4f7615b0e52c84ced72"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:46.601203Z","signature_b64":"qRUGrqdEsbddvEKCoSPNtR/Qxhgqbf/lkWw0Rs2Lt+7IYaWpO1jFe4hCbRNX79hc2I5vrgn18wiqQ/JdnebqAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9aa6b01cdc84c1ac80ce49deb76cef08ad9418f38b171dc1c0827b4e558542c1","last_reissued_at":"2026-07-05T09:05:46.600728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:46.600728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Phy124: Fast Physics-Driven 4D Content Generation from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiajing Lin, Min Jiang, Yongjie Hou, Yuzhou Tang, Zhenzhong Wang","submitted_at":"2024-09-11T10:41:46Z","abstract_excerpt":"4D content generation focuses on creating dynamic 3D objects that change over time. Existing methods primarily rely on pre-trained video diffusion models, utilizing sampling processes or reference videos. However, these approaches face significant challenges. Firstly, the generated 4D content often fails to adhere to real-world physics since video diffusion models do not incorporate physical priors. Secondly, the extensive sampling process and the large number of parameters in diffusion models result in exceedingly time-consuming generation processes. To address these issues, we introduce Phy1"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07179","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/2409.07179/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":"2409.07179","created_at":"2026-07-05T09:05:46.600784+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.07179v1","created_at":"2026-07-05T09:05:46.600784+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07179","created_at":"2026-07-05T09:05:46.600784+00:00"},{"alias_kind":"pith_short_12","alias_value":"TKTLAHG4QTA2","created_at":"2026-07-05T09:05:46.600784+00:00"},{"alias_kind":"pith_short_16","alias_value":"TKTLAHG4QTA2ZAGO","created_at":"2026-07-05T09:05:46.600784+00:00"},{"alias_kind":"pith_short_8","alias_value":"TKTLAHG4","created_at":"2026-07-05T09:05:46.600784+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09187","citing_title":"CP4D: Compositional Physics-aware 4D Scene Generation","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24962","citing_title":"Tempered Self-Similarity Alignment for Physically Plausible Video Generation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2601.00678","citing_title":"Pixel-to-4D: Camera-Controlled Image-to-Video Generation with Dynamic 3D Gaussians","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2601.00678","citing_title":"Pixel-to-4D: Camera-Controlled Image-to-Video Generation with Dynamic 3D Gaussians","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC","json":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC.json","graph_json":"https://pith.science/api/pith-number/TKTLAHG4QTA2ZAGOJHPLO3HPBC/graph.json","events_json":"https://pith.science/api/pith-number/TKTLAHG4QTA2ZAGOJHPLO3HPBC/events.json","paper":"https://pith.science/paper/TKTLAHG4"},"agent_actions":{"view_html":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC","download_json":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC.json","view_paper":"https://pith.science/paper/TKTLAHG4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.07179&json=true","fetch_graph":"https://pith.science/api/pith-number/TKTLAHG4QTA2ZAGOJHPLO3HPBC/graph.json","fetch_events":"https://pith.science/api/pith-number/TKTLAHG4QTA2ZAGOJHPLO3HPBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC/action/storage_attestation","attest_author":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC/action/author_attestation","sign_citation":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC/action/citation_signature","submit_replication":"https://pith.science/pith/TKTLAHG4QTA2ZAGOJHPLO3HPBC/action/replication_record"}},"created_at":"2026-07-05T09:05:46.600784+00:00","updated_at":"2026-07-05T09:05:46.600784+00:00"}