{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4OHOC2KHAFVTZUIIIMLBTR7BU6","short_pith_number":"pith:4OHOC2KH","schema_version":"1.0","canonical_sha256":"e38ee16947016b3cd108431619c7e1a7b51bd190e8fa63539039eab3edce1dbf","source":{"kind":"arxiv","id":"2303.08133","version":2},"attestation_state":"computed","paper":{"title":"MeshDiffusion: Score-based Generative 3D Mesh Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Derek Nowrouzezahrai, Liam Paull, Michael J. Black, Weiyang Liu, Yao Feng, Zhen Liu","submitted_at":"2023-03-14T17:59:01Z","abstract_excerpt":"We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overl"},"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":"2303.08133","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2023-03-14T17:59:01Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"4eef004302227640f3c7305ef17d30c5b21cdb5d4456db2499e8fcdb35e37c1f","abstract_canon_sha256":"f6a3df8988674160c2113b6bf0ed758b7117c0fc2e5b9f674bd2f36a2ea5ea46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:01:18.018716Z","signature_b64":"0kpeElYu++L1YOppPkcT8ZjjYRk4lYBJ9pbLiDjKopjocBz2pFB0JI09Aw7nDvAOtV13AI9tJJ18AeCpu+fTCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e38ee16947016b3cd108431619c7e1a7b51bd190e8fa63539039eab3edce1dbf","last_reissued_at":"2026-07-05T06:01:18.018231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:01:18.018231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MeshDiffusion: Score-based Generative 3D Mesh Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Derek Nowrouzezahrai, Liam Paull, Michael J. Black, Weiyang Liu, Yao Feng, Zhen Liu","submitted_at":"2023-03-14T17:59:01Z","abstract_excerpt":"We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.08133","kind":"arxiv","version":2},"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/2303.08133/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":"2303.08133","created_at":"2026-07-05T06:01:18.018291+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.08133v2","created_at":"2026-07-05T06:01:18.018291+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.08133","created_at":"2026-07-05T06:01:18.018291+00:00"},{"alias_kind":"pith_short_12","alias_value":"4OHOC2KHAFVT","created_at":"2026-07-05T06:01:18.018291+00:00"},{"alias_kind":"pith_short_16","alias_value":"4OHOC2KHAFVTZUII","created_at":"2026-07-05T06:01:18.018291+00:00"},{"alias_kind":"pith_short_8","alias_value":"4OHOC2KH","created_at":"2026-07-05T06:01:18.018291+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13364","citing_title":"VideoMDM: Towards 3D Human Motion Generation From 2D Supervision","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08957","citing_title":"Rethinking 3D Shape Generation: Diffusion over Superquadrics","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08440","citing_title":"GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24805","citing_title":"Fishbone: From One 3D Asset to a Million Controllable Edits","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2401.16764","citing_title":"BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2504.10466","citing_title":"Art3D: Training-Free 3D Generation from Flat-Colored Illustration","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19305","citing_title":"Mat\\'ern Noise for Triangulation-Agnostic Flow Matching on Meshes","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2507.15753","citing_title":"Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2308.16512","citing_title":"MVDream: Multi-view Diffusion for 3D Generation","ref_index":137,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6","json":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6.json","graph_json":"https://pith.science/api/pith-number/4OHOC2KHAFVTZUIIIMLBTR7BU6/graph.json","events_json":"https://pith.science/api/pith-number/4OHOC2KHAFVTZUIIIMLBTR7BU6/events.json","paper":"https://pith.science/paper/4OHOC2KH"},"agent_actions":{"view_html":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6","download_json":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6.json","view_paper":"https://pith.science/paper/4OHOC2KH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.08133&json=true","fetch_graph":"https://pith.science/api/pith-number/4OHOC2KHAFVTZUIIIMLBTR7BU6/graph.json","fetch_events":"https://pith.science/api/pith-number/4OHOC2KHAFVTZUIIIMLBTR7BU6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6/action/storage_attestation","attest_author":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6/action/author_attestation","sign_citation":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6/action/citation_signature","submit_replication":"https://pith.science/pith/4OHOC2KHAFVTZUIIIMLBTR7BU6/action/replication_record"}},"created_at":"2026-07-05T06:01:18.018291+00:00","updated_at":"2026-07-05T06:01:18.018291+00:00"}