{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RVZ3NPBQDBLKL6XHXYMCTTP527","short_pith_number":"pith:RVZ3NPBQ","schema_version":"1.0","canonical_sha256":"8d73b6bc301856a5fae7be1829cdfdd7dab9942d755322dae52b6af2c14307de","source":{"kind":"arxiv","id":"2311.15435","version":1},"attestation_state":"computed","paper":{"title":"Functional Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Biao Zhang, Peter Wonka","submitted_at":"2023-11-26T21:35:34Z","abstract_excerpt":"We propose a new class of generative diffusion models, called functional diffusion. In contrast to previous work, functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images, videos, audio, 3D shapes, deformations, \\etc, can be handled by the same framework with minimal changes. In addition, functional diffusion is especially suited for irregular data or data defined in non-standard domains. In o"},"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":"2311.15435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-26T21:35:34Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"3b0281bdbcb3806b2b9a330ac1e8d4255958df69c49d9f11a2cf6195838b1bf2","abstract_canon_sha256":"0fe6f266176230613830c4ed0fa86399596d1b29c3006cdf7fefe6c7b20c54f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:03.739195Z","signature_b64":"v9dB2NgS9ngfG8q++dOm7ORdxqwrvYQcmTyo9lIvGFXINg5vlIZlHulsdhtH0n4f5BXxjQXYWOaD/+slLf8oCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d73b6bc301856a5fae7be1829cdfdd7dab9942d755322dae52b6af2c14307de","last_reissued_at":"2026-07-05T07:17:03.738779Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:03.738779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Functional Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Biao Zhang, Peter Wonka","submitted_at":"2023-11-26T21:35:34Z","abstract_excerpt":"We propose a new class of generative diffusion models, called functional diffusion. In contrast to previous work, functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images, videos, audio, 3D shapes, deformations, \\etc, can be handled by the same framework with minimal changes. In addition, functional diffusion is especially suited for irregular data or data defined in non-standard domains. In o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15435","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/2311.15435/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":"2311.15435","created_at":"2026-07-05T07:17:03.738836+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.15435v1","created_at":"2026-07-05T07:17:03.738836+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15435","created_at":"2026-07-05T07:17:03.738836+00:00"},{"alias_kind":"pith_short_12","alias_value":"RVZ3NPBQDBLK","created_at":"2026-07-05T07:17:03.738836+00:00"},{"alias_kind":"pith_short_16","alias_value":"RVZ3NPBQDBLKL6XH","created_at":"2026-07-05T07:17:03.738836+00:00"},{"alias_kind":"pith_short_8","alias_value":"RVZ3NPBQ","created_at":"2026-07-05T07:17:03.738836+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11698","citing_title":"Fusion of multi-source precipitation records via coordinate-based generative model","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527","json":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527.json","graph_json":"https://pith.science/api/pith-number/RVZ3NPBQDBLKL6XHXYMCTTP527/graph.json","events_json":"https://pith.science/api/pith-number/RVZ3NPBQDBLKL6XHXYMCTTP527/events.json","paper":"https://pith.science/paper/RVZ3NPBQ"},"agent_actions":{"view_html":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527","download_json":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527.json","view_paper":"https://pith.science/paper/RVZ3NPBQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.15435&json=true","fetch_graph":"https://pith.science/api/pith-number/RVZ3NPBQDBLKL6XHXYMCTTP527/graph.json","fetch_events":"https://pith.science/api/pith-number/RVZ3NPBQDBLKL6XHXYMCTTP527/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527/action/storage_attestation","attest_author":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527/action/author_attestation","sign_citation":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527/action/citation_signature","submit_replication":"https://pith.science/pith/RVZ3NPBQDBLKL6XHXYMCTTP527/action/replication_record"}},"created_at":"2026-07-05T07:17:03.738836+00:00","updated_at":"2026-07-05T07:17:03.738836+00:00"}