{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5G43DGQ5V26WEESIL4RD6IWLUN","short_pith_number":"pith:5G43DGQ5","schema_version":"1.0","canonical_sha256":"e9b9b19a1daebd6212485f223f22cba37446ebb1a7e5d174d6343ecb8e13bd3c","source":{"kind":"arxiv","id":"2502.00708","version":1},"attestation_state":"computed","paper":{"title":"PhiP-G: Physics-Guided Text-to-3D Compositional Scene Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chao Wang, Qixuan Li, Yan Peng, Zongjin He","submitted_at":"2025-02-02T07:47:03Z","abstract_excerpt":"Text-to-3D asset generation has achieved significant optimization under the supervision of 2D diffusion priors. However, when dealing with compositional scenes, existing methods encounter several challenges: 1). failure to ensure that composite scene layouts comply with physical laws; 2). difficulty in accurately capturing the assets and relationships described in complex scene descriptions; 3). limited autonomous asset generation capabilities among layout approaches leveraging large language models (LLMs). To avoid these compromises, we propose a novel framework for compositional scene genera"},"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":"2502.00708","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-02T07:47:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"05b6fe9dbd05c8518785fb1589301d6242149db08b28d074311e244025183166","abstract_canon_sha256":"5979a4c158134340e16cee1f9322fd4be0602f6759bf9888f95fce1a66e937d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:37.465285Z","signature_b64":"TDDBSVNY4ygkQPyFdbHPXDc2hwLQAZ/mQ7GlvoU0RLYi9RcnhUBMfhGavo6fT8jzBqGt4Dou/1VNAlycx5p+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9b9b19a1daebd6212485f223f22cba37446ebb1a7e5d174d6343ecb8e13bd3c","last_reissued_at":"2026-07-05T10:08:37.464823Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:37.464823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhiP-G: Physics-Guided Text-to-3D Compositional Scene Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chao Wang, Qixuan Li, Yan Peng, Zongjin He","submitted_at":"2025-02-02T07:47:03Z","abstract_excerpt":"Text-to-3D asset generation has achieved significant optimization under the supervision of 2D diffusion priors. However, when dealing with compositional scenes, existing methods encounter several challenges: 1). failure to ensure that composite scene layouts comply with physical laws; 2). difficulty in accurately capturing the assets and relationships described in complex scene descriptions; 3). limited autonomous asset generation capabilities among layout approaches leveraging large language models (LLMs). To avoid these compromises, we propose a novel framework for compositional scene genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00708","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/2502.00708/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":"2502.00708","created_at":"2026-07-05T10:08:37.464879+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00708v1","created_at":"2026-07-05T10:08:37.464879+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00708","created_at":"2026-07-05T10:08:37.464879+00:00"},{"alias_kind":"pith_short_12","alias_value":"5G43DGQ5V26W","created_at":"2026-07-05T10:08:37.464879+00:00"},{"alias_kind":"pith_short_16","alias_value":"5G43DGQ5V26WEESI","created_at":"2026-07-05T10:08:37.464879+00:00"},{"alias_kind":"pith_short_8","alias_value":"5G43DGQ5","created_at":"2026-07-05T10:08:37.464879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03994","citing_title":"SimuScene: Simulation-Ready Compositional 3D Scene Reconstruction from a Single Image","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN","json":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN.json","graph_json":"https://pith.science/api/pith-number/5G43DGQ5V26WEESIL4RD6IWLUN/graph.json","events_json":"https://pith.science/api/pith-number/5G43DGQ5V26WEESIL4RD6IWLUN/events.json","paper":"https://pith.science/paper/5G43DGQ5"},"agent_actions":{"view_html":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN","download_json":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN.json","view_paper":"https://pith.science/paper/5G43DGQ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00708&json=true","fetch_graph":"https://pith.science/api/pith-number/5G43DGQ5V26WEESIL4RD6IWLUN/graph.json","fetch_events":"https://pith.science/api/pith-number/5G43DGQ5V26WEESIL4RD6IWLUN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN/action/storage_attestation","attest_author":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN/action/author_attestation","sign_citation":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN/action/citation_signature","submit_replication":"https://pith.science/pith/5G43DGQ5V26WEESIL4RD6IWLUN/action/replication_record"}},"created_at":"2026-07-05T10:08:37.464879+00:00","updated_at":"2026-07-05T10:08:37.464879+00:00"}