{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SDJO2XE5NWU5QLYLA7KL2JKAKO","short_pith_number":"pith:SDJO2XE5","schema_version":"1.0","canonical_sha256":"90d2ed5c9d6da9d82f0b07d4bd254053980673ef5efc22b86f151663245ade19","source":{"kind":"arxiv","id":"2605.14703","version":1},"attestation_state":"computed","paper":{"title":"Generating HDR Video from SDR Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Large generative video models can synthesize HDR sequences from casual SDR video by first predicting bracketed linear exposures and then merging them.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daisuke Iso, David B. Lindell, Feiran Li, Francesco Banterle, Jiacheng Li, Karanpreet Raja, Kiriakos N. Kutulakos, SaiKiran Tedla, Trevor Canham","submitted_at":"2026-05-14T11:21:10Z","abstract_excerpt":"The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persists without a convincing solution. We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models. We introduce a Multi-Exposure Video Model (MEVM) that can predict exposure-bracketed linear SDR video sequences from a single nonlinear SDR video input. We further propose a learnable Video Merging Model (VMM) that merges the predicted exposure-bracketed video into a high-quality HDR sequence"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.14703","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T11:21:10Z","cross_cats_sorted":[],"title_canon_sha256":"a8fa819b2c190cfbaeddf7a8ac6c347024a68e3c814030226f9154c383f6dfc1","abstract_canon_sha256":"ea1715793027dab2a724f4f62b63655eb39e37d65554ac001b9438735a24890e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:59.310594Z","signature_b64":"uunASbx+pFz9ks8iHzv8XWFwpXKy9tMOfWxfNjbCV1RWQ1Hd2zME42kWfdyMzWmDy/6+O3s5fh+iv9QOS9fTCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90d2ed5c9d6da9d82f0b07d4bd254053980673ef5efc22b86f151663245ade19","last_reissued_at":"2026-05-17T23:38:59.309851Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:59.309851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generating HDR Video from SDR Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Large generative video models can synthesize HDR sequences from casual SDR video by first predicting bracketed linear exposures and then merging them.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daisuke Iso, David B. Lindell, Feiran Li, Francesco Banterle, Jiacheng Li, Karanpreet Raja, Kiriakos N. Kutulakos, SaiKiran Tedla, Trevor Canham","submitted_at":"2026-05-14T11:21:10Z","abstract_excerpt":"The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persists without a convincing solution. We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models. We introduce a Multi-Exposure Video Model (MEVM) that can predict exposure-bracketed linear SDR video sequences from a single nonlinear SDR video input. We further propose a learnable Video Merging Model (VMM) that merges the predicted exposure-bracketed video into a high-quality HDR sequence"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That large-scale generative video models can reliably predict accurate exposure-bracketed linear SDR sequences from a single nonlinear SDR input without introducing artifacts or inconsistencies.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Large generative video models can synthesize HDR sequences from casual SDR video by first predicting bracketed linear exposures and then merging them.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0632aa637af76f5deae9e1713fc4f1fbea3ed7a6c4d6f4a3dee67adf36d84091"},"source":{"id":"2605.14703","kind":"arxiv","version":1},"verdict":{"id":"5e5f66c9-39fd-4b76-87a8-626dd3c901e6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:04:39.597770Z","strongest_claim":"our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films.","one_line_summary":"A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That large-scale generative video models can reliably predict accurate exposure-bracketed linear SDR sequences from a single nonlinear SDR input without introducing artifacts or inconsistencies.","pith_extraction_headline":"Large generative video models can synthesize HDR sequences from casual SDR video by first predicting bracketed linear exposures and then merging them."},"references":{"count":300,"sample":[{"doi":"","year":2025,"title":"BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models , author=. 2025 , eprint=","work_id":"c5a22e1c-14b1-4716-b126-ef8e355868ac","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"History-Guided Video Diffusion , author=. 2025 , booktitle=","work_id":"938de7c5-4131-44d1-9aec-b6f9171115cb","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Diffusion forcing: Next-token prediction meets full-sequence diffusion , author=. NeurIPS , year=","work_id":"401ca5cf-33f3-4cdb-881e-1127a7cc0e7b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"High dynamic range imaging: Spatially varying pixel exposures , author=. CVPR , year=","work_id":"cec19af7-0b2b-4a26-ade2-559d17b0f5d1","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Burst photography for high dynamic range and low-light imaging on mobile cameras , author=. ToG , volume=","work_id":"7626adf3-c6b4-476f-bc24-bf89d822ad29","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":300,"snapshot_sha256":"df46f1e8bed2f95d24a613fa2f70eded973be74ace0ee24ca951e9228e7785f3","internal_anchors":7},"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":"2605.14703","created_at":"2026-05-17T23:38:59.309984+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.14703v1","created_at":"2026-05-17T23:38:59.309984+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14703","created_at":"2026-05-17T23:38:59.309984+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDJO2XE5NWU5","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDJO2XE5NWU5QLYL","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDJO2XE5","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO","json":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO.json","graph_json":"https://pith.science/api/pith-number/SDJO2XE5NWU5QLYLA7KL2JKAKO/graph.json","events_json":"https://pith.science/api/pith-number/SDJO2XE5NWU5QLYLA7KL2JKAKO/events.json","paper":"https://pith.science/paper/SDJO2XE5"},"agent_actions":{"view_html":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO","download_json":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO.json","view_paper":"https://pith.science/paper/SDJO2XE5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.14703&json=true","fetch_graph":"https://pith.science/api/pith-number/SDJO2XE5NWU5QLYLA7KL2JKAKO/graph.json","fetch_events":"https://pith.science/api/pith-number/SDJO2XE5NWU5QLYLA7KL2JKAKO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO/action/storage_attestation","attest_author":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO/action/author_attestation","sign_citation":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO/action/citation_signature","submit_replication":"https://pith.science/pith/SDJO2XE5NWU5QLYLA7KL2JKAKO/action/replication_record"}},"created_at":"2026-05-17T23:38:59.309984+00:00","updated_at":"2026-05-17T23:38:59.309984+00:00"}