{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:STSLZOHSI7VBDTIJFG5DMWLYRU","short_pith_number":"pith:STSLZOHS","schema_version":"1.0","canonical_sha256":"94e4bcb8f247ea11cd0929ba3659788d3a0f0e2458f2d6336e4e47dd8a95d567","source":{"kind":"arxiv","id":"2407.08231","version":1},"attestation_state":"computed","paper":{"title":"E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohan Yu, Boxin Shi, Jinxiu Liang, Yiming Han, Yixin Yang","submitted_at":"2024-07-11T07:10:58Z","abstract_excerpt":"Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities. Traditional regression-based deep learning methods fall short in perceptual quality, offering deterministic and often unrealistic reconstructions. In this paper, we introduce diffusion models to events-to-video reconstruction, achieving colorful, realistic, and perceptually superior video generation from achromatic events. Powered by the image generation abi"},"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":"2407.08231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-11T07:10:58Z","cross_cats_sorted":[],"title_canon_sha256":"72955d1043bab2c70100cdb3bee4a3fc966e99934c5f5aa84aeb4a1e4953544e","abstract_canon_sha256":"f53c73a2bd857a1d49e88da74c4457e2782a2c7ae3c45d64bb4bc466995c1074"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:45.242268Z","signature_b64":"Py2+c539gkmZIISsZQZIwagOr/OZiaj09TowF36gTUkP0F42yNZB8xc6x93wCp/kae8er6uzdMqU9S/b9tItCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94e4bcb8f247ea11cd0929ba3659788d3a0f0e2458f2d6336e4e47dd8a95d567","last_reissued_at":"2026-07-05T08:42:45.241850Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:45.241850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohan Yu, Boxin Shi, Jinxiu Liang, Yiming Han, Yixin Yang","submitted_at":"2024-07-11T07:10:58Z","abstract_excerpt":"Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities. Traditional regression-based deep learning methods fall short in perceptual quality, offering deterministic and often unrealistic reconstructions. In this paper, we introduce diffusion models to events-to-video reconstruction, achieving colorful, realistic, and perceptually superior video generation from achromatic events. Powered by the image generation abi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08231","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/2407.08231/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":"2407.08231","created_at":"2026-07-05T08:42:45.241908+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.08231v1","created_at":"2026-07-05T08:42:45.241908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08231","created_at":"2026-07-05T08:42:45.241908+00:00"},{"alias_kind":"pith_short_12","alias_value":"STSLZOHSI7VB","created_at":"2026-07-05T08:42:45.241908+00:00"},{"alias_kind":"pith_short_16","alias_value":"STSLZOHSI7VBDTIJ","created_at":"2026-07-05T08:42:45.241908+00:00"},{"alias_kind":"pith_short_8","alias_value":"STSLZOHS","created_at":"2026-07-05T08:42:45.241908+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08770","citing_title":"LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2602.19202","citing_title":"UniE2F: A Unified Diffusion Framework for Event-to-Frame Reconstruction with Video Foundation Models","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU","json":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU.json","graph_json":"https://pith.science/api/pith-number/STSLZOHSI7VBDTIJFG5DMWLYRU/graph.json","events_json":"https://pith.science/api/pith-number/STSLZOHSI7VBDTIJFG5DMWLYRU/events.json","paper":"https://pith.science/paper/STSLZOHS"},"agent_actions":{"view_html":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU","download_json":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU.json","view_paper":"https://pith.science/paper/STSLZOHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.08231&json=true","fetch_graph":"https://pith.science/api/pith-number/STSLZOHSI7VBDTIJFG5DMWLYRU/graph.json","fetch_events":"https://pith.science/api/pith-number/STSLZOHSI7VBDTIJFG5DMWLYRU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU/action/storage_attestation","attest_author":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU/action/author_attestation","sign_citation":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU/action/citation_signature","submit_replication":"https://pith.science/pith/STSLZOHSI7VBDTIJFG5DMWLYRU/action/replication_record"}},"created_at":"2026-07-05T08:42:45.241908+00:00","updated_at":"2026-07-05T08:42:45.241908+00:00"}