{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:EPGMXJHW2SG6CTVJWSDKCTOBA5","short_pith_number":"pith:EPGMXJHW","canonical_record":{"source":{"id":"2503.13272","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-17T15:24:04Z","cross_cats_sorted":[],"title_canon_sha256":"f9e412adea705f77a1046360ecca0ff586aa4a4c3bbf2176f4c74331f8965136","abstract_canon_sha256":"4905dc5f8c3f508b09787e6b9d44788724e507297fafa62e18bec0a8fc45ea9c"},"schema_version":"1.0"},"canonical_sha256":"23cccba4f6d48de14ea9b486a14dc1074c63162af4808c9dcb1d6a46f8eae211","source":{"kind":"arxiv","id":"2503.13272","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13272","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13272v1","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13272","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_12","alias_value":"EPGMXJHW2SG6","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_16","alias_value":"EPGMXJHW2SG6CTVJ","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_8","alias_value":"EPGMXJHW","created_at":"2026-07-05T10:32:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:EPGMXJHW2SG6CTVJWSDKCTOBA5","target":"record","payload":{"canonical_record":{"source":{"id":"2503.13272","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-17T15:24:04Z","cross_cats_sorted":[],"title_canon_sha256":"f9e412adea705f77a1046360ecca0ff586aa4a4c3bbf2176f4c74331f8965136","abstract_canon_sha256":"4905dc5f8c3f508b09787e6b9d44788724e507297fafa62e18bec0a8fc45ea9c"},"schema_version":"1.0"},"canonical_sha256":"23cccba4f6d48de14ea9b486a14dc1074c63162af4808c9dcb1d6a46f8eae211","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:57.121494Z","signature_b64":"EvbM6igX2R/t2hY5qRjBBmLBwvL2qh024quu2nlbpbeQXbWcKsX28xSBb0TxNyuG4wLRhhvk+pyFVRVS6y/yAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23cccba4f6d48de14ea9b486a14dc1074c63162af4808c9dcb1d6a46f8eae211","last_reissued_at":"2026-07-05T10:32:57.120939Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:57.120939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.13272","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:32:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fy9ULCckxnV40y3rMTaGbiSd6StjCl7Xu+xvZyUjTkG8utgluPyEyAZ4dcoQBx5bEuK/MM+4YWgbdl7eiFFmDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:45:45.183011Z"},"content_sha256":"f3f39dbef3bddea3b5d0fa471295682327cb67bf89e4829079d425e52d340d37","schema_version":"1.0","event_id":"sha256:f3f39dbef3bddea3b5d0fa471295682327cb67bf89e4829079d425e52d340d37"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:EPGMXJHW2SG6CTVJWSDKCTOBA5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Gaussian Splatting: Generating 3D Scenes with Video Diffusion Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Katja Schwarz, Norman Mueller, Peter Kontschieder","submitted_at":"2025-03-17T15:24:04Z","abstract_excerpt":"Synthesizing consistent and photorealistic 3D scenes is an open problem in computer vision. Video diffusion models generate impressive videos but cannot directly synthesize 3D representations, i.e., lack 3D consistency in the generated sequences. In addition, directly training generative 3D models is challenging due to a lack of 3D training data at scale. In this work, we present Generative Gaussian Splatting (GGS) -- a novel approach that integrates a 3D representation with a pre-trained latent video diffusion model. Specifically, our model synthesizes a feature field parameterized via 3D Gau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13272","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/2503.13272/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":"2026-07-05T10:32:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SwQFTpTt2n7lm1+C/Gmx2m1As1ONeHAQihipd787yVMrIVDIH9im4l/HFN/Jbzdfc1iGu1dI9eonW7QW0gG3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:45:45.183966Z"},"content_sha256":"fdf5022a1793c5b71408922b1f0e23c2e6813b4be28eae5b36cfada8dcfee57c","schema_version":"1.0","event_id":"sha256:fdf5022a1793c5b71408922b1f0e23c2e6813b4be28eae5b36cfada8dcfee57c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/bundle.json","state_url":"https://pith.science/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/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":"2026-08-07T01:45:45Z","links":{"resolver":"https://pith.science/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5","bundle":"https://pith.science/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/bundle.json","state":"https://pith.science/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EPGMXJHW2SG6CTVJWSDKCTOBA5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EPGMXJHW2SG6CTVJWSDKCTOBA5","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":"4905dc5f8c3f508b09787e6b9d44788724e507297fafa62e18bec0a8fc45ea9c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-17T15:24:04Z","title_canon_sha256":"f9e412adea705f77a1046360ecca0ff586aa4a4c3bbf2176f4c74331f8965136"},"schema_version":"1.0","source":{"id":"2503.13272","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13272","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13272v1","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13272","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_12","alias_value":"EPGMXJHW2SG6","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_16","alias_value":"EPGMXJHW2SG6CTVJ","created_at":"2026-07-05T10:32:57Z"},{"alias_kind":"pith_short_8","alias_value":"EPGMXJHW","created_at":"2026-07-05T10:32:57Z"}],"graph_snapshots":[{"event_id":"sha256:fdf5022a1793c5b71408922b1f0e23c2e6813b4be28eae5b36cfada8dcfee57c","target":"graph","created_at":"2026-07-05T10:32:57Z","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/2503.13272/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Synthesizing consistent and photorealistic 3D scenes is an open problem in computer vision. Video diffusion models generate impressive videos but cannot directly synthesize 3D representations, i.e., lack 3D consistency in the generated sequences. In addition, directly training generative 3D models is challenging due to a lack of 3D training data at scale. In this work, we present Generative Gaussian Splatting (GGS) -- a novel approach that integrates a 3D representation with a pre-trained latent video diffusion model. Specifically, our model synthesizes a feature field parameterized via 3D Gau","authors_text":"Katja Schwarz, Norman Mueller, Peter Kontschieder","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-17T15:24:04Z","title":"Generative Gaussian Splatting: Generating 3D Scenes with Video Diffusion Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13272","kind":"arxiv","version":1},"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:f3f39dbef3bddea3b5d0fa471295682327cb67bf89e4829079d425e52d340d37","target":"record","created_at":"2026-07-05T10:32:57Z","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":"4905dc5f8c3f508b09787e6b9d44788724e507297fafa62e18bec0a8fc45ea9c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-17T15:24:04Z","title_canon_sha256":"f9e412adea705f77a1046360ecca0ff586aa4a4c3bbf2176f4c74331f8965136"},"schema_version":"1.0","source":{"id":"2503.13272","kind":"arxiv","version":1}},"canonical_sha256":"23cccba4f6d48de14ea9b486a14dc1074c63162af4808c9dcb1d6a46f8eae211","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23cccba4f6d48de14ea9b486a14dc1074c63162af4808c9dcb1d6a46f8eae211","first_computed_at":"2026-07-05T10:32:57.120939Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:32:57.120939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EvbM6igX2R/t2hY5qRjBBmLBwvL2qh024quu2nlbpbeQXbWcKsX28xSBb0TxNyuG4wLRhhvk+pyFVRVS6y/yAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:32:57.121494Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.13272","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3f39dbef3bddea3b5d0fa471295682327cb67bf89e4829079d425e52d340d37","sha256:fdf5022a1793c5b71408922b1f0e23c2e6813b4be28eae5b36cfada8dcfee57c"],"state_sha256":"855834dfbc5df1ce817a7ed129188673cb8458b5e46fc87b971d40aeb06e1a23"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IKDI8Tb6g+77ufXWlUvgAmBfR1vbDWJKytoBraZ5irznLQXjpyIrAiVfaEvUqt3mbkHLVwBqeXyRrErj9MVnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T01:45:45.247950Z","bundle_sha256":"98cbf285d7307f0dc4784ac19d8056634136221f43c0dcf4ac79fff7e973afe5"}}