{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:LXGHW2BGAMI2D4MGOGDQHCFKV2","short_pith_number":"pith:LXGHW2BG","canonical_record":{"source":{"id":"2309.03453","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-07T02:28:04Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"c0bc6d4229afcbf80b4af494079b0f0bced3c98ab03cc885eaa2619d13f72c6c","abstract_canon_sha256":"6ffd95ba3b94bbd19c6b9c09224aaf9a7a93456fad07c7adb222496d882b3eed"},"schema_version":"1.0"},"canonical_sha256":"5dcc7b68260311a1f18671870388aaae97b3014e62c7c141ea4c18597b849318","source":{"kind":"arxiv","id":"2309.03453","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03453","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03453v2","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03453","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"LXGHW2BGAMI2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"LXGHW2BGAMI2D4MG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"LXGHW2BG","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:LXGHW2BGAMI2D4MGOGDQHCFKV2","target":"record","payload":{"canonical_record":{"source":{"id":"2309.03453","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-07T02:28:04Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"c0bc6d4229afcbf80b4af494079b0f0bced3c98ab03cc885eaa2619d13f72c6c","abstract_canon_sha256":"6ffd95ba3b94bbd19c6b9c09224aaf9a7a93456fad07c7adb222496d882b3eed"},"schema_version":"1.0"},"canonical_sha256":"5dcc7b68260311a1f18671870388aaae97b3014e62c7c141ea4c18597b849318","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:48.178421Z","signature_b64":"kSrdTCpN3h2oF1foLxa4eBxvNCX8eQi6PpQmVuGJdxvToMAlsE+ou2or7oYsS74zPBPncG3eEIRETfQoj0IQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5dcc7b68260311a1f18671870388aaae97b3014e62c7c141ea4c18597b849318","last_reissued_at":"2026-05-17T23:38:48.177841Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:48.177841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.03453","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":"2026-05-17T23:38:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qziqr9r6Ma68PtIyDYl4TKEBxZ04OBVkim/NKuYfNFmGpVd6JzdtvD2pkervay52hKaklot/FisB7ZAHYV8xAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:25:19.299216Z"},"content_sha256":"f5ba94034178c3ba63e50e86dd69bd381dd720f8ec0f493680dedddcc9a3a713","schema_version":"1.0","event_id":"sha256:f5ba94034178c3ba63e50e86dd69bd381dd720f8ec0f493680dedddcc9a3a713"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:LXGHW2BGAMI2D4MGOGDQHCFKV2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SyncDreamer: Generating Multiview-consistent Images from a Single-view Image","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process.","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Cheng Lin, Lingjie Liu, Taku Komura, Wenping Wang, Xiaoxiao Long, Yuan Liu, Zijiao Zeng","submitted_at":"2023-09-07T02:28:04Z","abstract_excerpt":"In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a s"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The 3D-aware feature attention mechanism successfully correlates corresponding features across views without introducing new inconsistencies or artifacts during the joint reverse diffusion process.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6a71eaed771d41734b94558cb31517e09f0c28dcf5415fa48552c39ab7a00e44"},"source":{"id":"2309.03453","kind":"arxiv","version":2},"verdict":{"id":"284650cd-755d-428e-9662-45d10de30678","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T10:31:44.910240Z","strongest_claim":"SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.","one_line_summary":"SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The 3D-aware feature attention mechanism successfully correlates corresponding features across views without introducing new inconsistencies or artifacts during the joint reverse diffusion process.","pith_extraction_headline":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process."},"references":{"count":46,"sample":[{"doi":"","year":null,"title":"Re-imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond","work_id":"1040fcd2-7a4c-4a7a-8803-bad82a00eea4","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Multidiffusion: Fusing diffusion paths for controlled image generation","work_id":"1a06e9b7-e97f-4665-ba6e-55301822d3b6","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"ShapeNet: An Information-Rich 3D Model Repository","work_id":"b2ac5b60-daa9-435b-9369-12271e126edd","ref_index":3,"cited_arxiv_id":"1512.03012","is_internal_anchor":true},{"doi":"","year":null,"title":"Single- stage diffusion nerf: A unified approach to 3d generation and reconstruction","work_id":"f4a4c59d-3c8e-4a0d-97cd-a540bc93203f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Objaverse-XL: A Universe of 10M+ 3D Objects","work_id":"1c5475ad-d1ec-4de1-8670-b8cd5a4c85d3","ref_index":5,"cited_arxiv_id":"2307.05663","is_internal_anchor":true}],"resolved_work":46,"snapshot_sha256":"55f35f9925fd55672eba4a2833bff437bffffbb1e69aed2944add4e63b8d372f","internal_anchors":7},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c16a268164c24fa21927a81e9233b242eabba7083285aaed2820eeed18a8ee81"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"284650cd-755d-428e-9662-45d10de30678"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fml7YxjkebORRpFR3RcJ319d3dXxAiEWFHZ8W2c2FBeX1t8CBsc6xjsUEEG5C+HMdDln3Z5SXzuQrXlWMb+3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:25:19.300147Z"},"content_sha256":"7c7f4743542e9fa67d1ae4b542a0a9cfa8d143c3a120f1eeb921658f046d01f5","schema_version":"1.0","event_id":"sha256:7c7f4743542e9fa67d1ae4b542a0a9cfa8d143c3a120f1eeb921658f046d01f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/bundle.json","state_url":"https://pith.science/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/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":"2026-05-25T13:25:19Z","links":{"resolver":"https://pith.science/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2","bundle":"https://pith.science/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/bundle.json","state":"https://pith.science/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LXGHW2BGAMI2D4MGOGDQHCFKV2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LXGHW2BGAMI2D4MGOGDQHCFKV2","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":"6ffd95ba3b94bbd19c6b9c09224aaf9a7a93456fad07c7adb222496d882b3eed","cross_cats_sorted":["cs.AI","cs.GR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-07T02:28:04Z","title_canon_sha256":"c0bc6d4229afcbf80b4af494079b0f0bced3c98ab03cc885eaa2619d13f72c6c"},"schema_version":"1.0","source":{"id":"2309.03453","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03453","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03453v2","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03453","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"LXGHW2BGAMI2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"LXGHW2BGAMI2D4MG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"LXGHW2BG","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:7c7f4743542e9fa67d1ae4b542a0a9cfa8d143c3a120f1eeb921658f046d01f5","target":"graph","created_at":"2026-05-17T23:38:48Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The 3D-aware feature attention mechanism successfully correlates corresponding features across views without introducing new inconsistencies or artifacts during the joint reverse diffusion process."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process."}],"snapshot_sha256":"6a71eaed771d41734b94558cb31517e09f0c28dcf5415fa48552c39ab7a00e44"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c16a268164c24fa21927a81e9233b242eabba7083285aaed2820eeed18a8ee81"},"paper":{"abstract_excerpt":"In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a s","authors_text":"Cheng Lin, Lingjie Liu, Taku Komura, Wenping Wang, Xiaoxiao Long, Yuan Liu, Zijiao Zeng","cross_cats":["cs.AI","cs.GR"],"headline":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-07T02:28:04Z","title":"SyncDreamer: Generating Multiview-consistent Images from a Single-view Image"},"references":{"count":46,"internal_anchors":7,"resolved_work":46,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Re-imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond","work_id":"1040fcd2-7a4c-4a7a-8803-bad82a00eea4","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Multidiffusion: Fusing diffusion paths for controlled image generation","work_id":"1a06e9b7-e97f-4665-ba6e-55301822d3b6","year":null},{"cited_arxiv_id":"1512.03012","doi":"","is_internal_anchor":true,"ref_index":3,"title":"ShapeNet: An Information-Rich 3D Model Repository","work_id":"b2ac5b60-daa9-435b-9369-12271e126edd","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Single- stage diffusion nerf: A unified approach to 3d generation and reconstruction","work_id":"f4a4c59d-3c8e-4a0d-97cd-a540bc93203f","year":null},{"cited_arxiv_id":"2307.05663","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Objaverse-XL: A Universe of 10M+ 3D Objects","work_id":"1c5475ad-d1ec-4de1-8670-b8cd5a4c85d3","year":2024}],"snapshot_sha256":"55f35f9925fd55672eba4a2833bff437bffffbb1e69aed2944add4e63b8d372f"},"source":{"id":"2309.03453","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T10:31:44.910240Z","id":"284650cd-755d-428e-9662-45d10de30678","model_set":{"reader":"grok-4.3"},"one_line_summary":"SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"SyncDreamer generates multiple consistent views of an object from one input image by synchronizing their diffusion process.","strongest_claim":"SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.","weakest_assumption":"The 3D-aware feature attention mechanism successfully correlates corresponding features across views without introducing new inconsistencies or artifacts during the joint reverse diffusion process."}},"verdict_id":"284650cd-755d-428e-9662-45d10de30678"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f5ba94034178c3ba63e50e86dd69bd381dd720f8ec0f493680dedddcc9a3a713","target":"record","created_at":"2026-05-17T23:38:48Z","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":"6ffd95ba3b94bbd19c6b9c09224aaf9a7a93456fad07c7adb222496d882b3eed","cross_cats_sorted":["cs.AI","cs.GR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-07T02:28:04Z","title_canon_sha256":"c0bc6d4229afcbf80b4af494079b0f0bced3c98ab03cc885eaa2619d13f72c6c"},"schema_version":"1.0","source":{"id":"2309.03453","kind":"arxiv","version":2}},"canonical_sha256":"5dcc7b68260311a1f18671870388aaae97b3014e62c7c141ea4c18597b849318","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5dcc7b68260311a1f18671870388aaae97b3014e62c7c141ea4c18597b849318","first_computed_at":"2026-05-17T23:38:48.177841Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:48.177841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kSrdTCpN3h2oF1foLxa4eBxvNCX8eQi6PpQmVuGJdxvToMAlsE+ou2or7oYsS74zPBPncG3eEIRETfQoj0IQBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:48.178421Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.03453","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f5ba94034178c3ba63e50e86dd69bd381dd720f8ec0f493680dedddcc9a3a713","sha256:7c7f4743542e9fa67d1ae4b542a0a9cfa8d143c3a120f1eeb921658f046d01f5"],"state_sha256":"8bc91bdef363f8d12dbe6d5e54bd28a0a1ef34212bc0d5e10666669a03cf1929"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b5PNoQZXS53SwwK9Xf3IqBzOR7AIiB8k4FHNbBIwfFF6TcCf6baLDuH0JqEbfU3Ink6i/P98Biep8kboY+RTCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T13:25:19.307908Z","bundle_sha256":"8fc0305f256d8348479f6e3f9e73c71fc5fd7c91171171b8428fe16886cffc35"}}