{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZPZBDV5RAKU6RLK6PSZXE6AD7H","short_pith_number":"pith:ZPZBDV5R","canonical_record":{"source":{"id":"2505.17721","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T10:38:05Z","cross_cats_sorted":[],"title_canon_sha256":"f41de7aba9ce027212bffb97f31435a00ae7b9203f77a40a3ea3dd1a513286de","abstract_canon_sha256":"6938dc64f03a3b4e0e3705e56b5429e64e54b9aa9da06380608b19182f2211fc"},"schema_version":"1.0"},"canonical_sha256":"cbf211d7b102a9e8ad5e7cb3727803f9ca63ae2506c091dbc6ddfab491e8cde0","source":{"kind":"arxiv","id":"2505.17721","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.17721","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"2505.17721v2","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17721","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZPZBDV5RAKU6","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_16","alias_value":"ZPZBDV5RAKU6RLK6","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_8","alias_value":"ZPZBDV5R","created_at":"2026-07-05T11:33:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZPZBDV5RAKU6RLK6PSZXE6AD7H","target":"record","payload":{"canonical_record":{"source":{"id":"2505.17721","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T10:38:05Z","cross_cats_sorted":[],"title_canon_sha256":"f41de7aba9ce027212bffb97f31435a00ae7b9203f77a40a3ea3dd1a513286de","abstract_canon_sha256":"6938dc64f03a3b4e0e3705e56b5429e64e54b9aa9da06380608b19182f2211fc"},"schema_version":"1.0"},"canonical_sha256":"cbf211d7b102a9e8ad5e7cb3727803f9ca63ae2506c091dbc6ddfab491e8cde0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:05.132600Z","signature_b64":"zUpPjH5OeWhOMei8/7zaLc6escy4hODczmAOOBas1Pd6FOsALSL6eRdTVvy6iytvC10j3/PMCpmov+fqAn5FDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbf211d7b102a9e8ad5e7cb3727803f9ca63ae2506c091dbc6ddfab491e8cde0","last_reissued_at":"2026-07-05T11:33:05.132102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:05.132102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.17721","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"54AYXpAzp6gObXP1RvsXMegkoSzlvRPE6t+pl35NgAxuHAmStIRcJges6bc2Pm2CKHOxTFt7pCZQm/g7iUkAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:43:02.071897Z"},"content_sha256":"566ded184440f3833b741e24a9303f4ef573a45427c7835ec47082d558700f7e","schema_version":"1.0","event_id":"sha256:566ded184440f3833b741e24a9303f4ef573a45427c7835ec47082d558700f7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZPZBDV5RAKU6RLK6PSZXE6AD7H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dekai Zhu, Slobodan Ilic, Stefan Gavranovic, Yan Di","submitted_at":"2025-05-23T10:38:05Z","abstract_excerpt":"Denoising diffusion probabilistic models have achieved significant success in point cloud generation, enabling numerous downstream applications, such as generative data augmentation and 3D model editing. However, little attention has been given to generating point clouds with point-wise segmentation labels, as well as to developing evaluation metrics for this task. Therefore, in this paper, we present SeaLion, a novel diffusion model designed to generate high-quality and diverse point clouds with fine-grained segmentation labels. Specifically, we introduce the semantic part-aware latent point "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17721","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/2505.17721/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yLhGbn1U8bPQdPZeZbLJAuRVw2FUj3abOgVfkzotXIJPCtKtguvq/dmw7WhBUHJcnd6UUSfdH0/ufcNg+ZIDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:43:02.072267Z"},"content_sha256":"5756d17c647b1d0ab07c0f0e0151968e83fe2e8301369fefef1d03278a4d5879","schema_version":"1.0","event_id":"sha256:5756d17c647b1d0ab07c0f0e0151968e83fe2e8301369fefef1d03278a4d5879"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/bundle.json","state_url":"https://pith.science/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T17:43:02Z","links":{"resolver":"https://pith.science/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H","bundle":"https://pith.science/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/bundle.json","state":"https://pith.science/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZPZBDV5RAKU6RLK6PSZXE6AD7H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZPZBDV5RAKU6RLK6PSZXE6AD7H","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6938dc64f03a3b4e0e3705e56b5429e64e54b9aa9da06380608b19182f2211fc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T10:38:05Z","title_canon_sha256":"f41de7aba9ce027212bffb97f31435a00ae7b9203f77a40a3ea3dd1a513286de"},"schema_version":"1.0","source":{"id":"2505.17721","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.17721","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"2505.17721v2","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17721","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZPZBDV5RAKU6","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_16","alias_value":"ZPZBDV5RAKU6RLK6","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_8","alias_value":"ZPZBDV5R","created_at":"2026-07-05T11:33:05Z"}],"graph_snapshots":[{"event_id":"sha256:5756d17c647b1d0ab07c0f0e0151968e83fe2e8301369fefef1d03278a4d5879","target":"graph","created_at":"2026-07-05T11:33:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.17721/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Denoising diffusion probabilistic models have achieved significant success in point cloud generation, enabling numerous downstream applications, such as generative data augmentation and 3D model editing. However, little attention has been given to generating point clouds with point-wise segmentation labels, as well as to developing evaluation metrics for this task. Therefore, in this paper, we present SeaLion, a novel diffusion model designed to generate high-quality and diverse point clouds with fine-grained segmentation labels. Specifically, we introduce the semantic part-aware latent point ","authors_text":"Dekai Zhu, Slobodan Ilic, Stefan Gavranovic, Yan Di","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T10:38:05Z","title":"SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17721","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:566ded184440f3833b741e24a9303f4ef573a45427c7835ec47082d558700f7e","target":"record","created_at":"2026-07-05T11:33:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6938dc64f03a3b4e0e3705e56b5429e64e54b9aa9da06380608b19182f2211fc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T10:38:05Z","title_canon_sha256":"f41de7aba9ce027212bffb97f31435a00ae7b9203f77a40a3ea3dd1a513286de"},"schema_version":"1.0","source":{"id":"2505.17721","kind":"arxiv","version":2}},"canonical_sha256":"cbf211d7b102a9e8ad5e7cb3727803f9ca63ae2506c091dbc6ddfab491e8cde0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cbf211d7b102a9e8ad5e7cb3727803f9ca63ae2506c091dbc6ddfab491e8cde0","first_computed_at":"2026-07-05T11:33:05.132102Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:05.132102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zUpPjH5OeWhOMei8/7zaLc6escy4hODczmAOOBas1Pd6FOsALSL6eRdTVvy6iytvC10j3/PMCpmov+fqAn5FDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:05.132600Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.17721","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:566ded184440f3833b741e24a9303f4ef573a45427c7835ec47082d558700f7e","sha256:5756d17c647b1d0ab07c0f0e0151968e83fe2e8301369fefef1d03278a4d5879"],"state_sha256":"a72510dc0219fb53a6c6fa6c0ad86af36db69e8a562b3f48a1ac0e75654309ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uhed1M3TbV548gOE4rVNQu1xqTHplggJ0cQGMNQBbaMHLW3nnW4WxxM+rnP3swlNgV5IGtFQQIBAmrqGxtSBBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:43:02.074183Z","bundle_sha256":"6da67c5df0f7d0402b38a9903180d355fa640f73e768faa03b13cd047d6b030e"}}