{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TUR6IQMMR5AQRIOLFFOKO7YHGO","short_pith_number":"pith:TUR6IQMM","schema_version":"1.0","canonical_sha256":"9d23e4418c8f4108a1cb295ca77f0733a8db5e0149f3c77faad84e3f14c249b8","source":{"kind":"arxiv","id":"2501.03699","version":2},"attestation_state":"computed","paper":{"title":"Motion-Aware Generative Frame Interpolation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guozhen Zhang, Kai Ma, Limin Wang, Xiaotong Zhao, Yuhan Zhu, Yutao Cui","submitted_at":"2025-01-07T11:03:43Z","abstract_excerpt":"Flow-based frame interpolation methods ensure motion stability through estimated intermediate flow but often introduce severe artifacts in complex motion regions. Recent generative approaches, boosted by large-scale pre-trained video generation models, show promise in handling intricate scenes. However, they frequently produce unstable motion and content inconsistencies due to the absence of explicit motion trajectory constraints. To address these challenges, we propose Motion-aware Generative frame interpolation (MoG) that synergizes intermediate flow guidance with generative capacities to en"},"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":"2501.03699","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-07T11:03:43Z","cross_cats_sorted":[],"title_canon_sha256":"b2c18492ca9e5db0cc0caa77f8943c74b35bf42004ec6f57369eeed2d2ed02f6","abstract_canon_sha256":"6f51597411d7b1baed4dc5b17f513f7038be43f6145897e25a0733341ab3a46a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:42.813408Z","signature_b64":"iA8/a5bCXgXhcDKoLMGeJCFIYiZ4B1hlOFbzw8FRbELTh1/fYWl45IUE2WQWjh4NPgg++BhFU+5k2mcXyDjBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d23e4418c8f4108a1cb295ca77f0733a8db5e0149f3c77faad84e3f14c249b8","last_reissued_at":"2026-07-05T10:26:42.812826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:42.812826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion-Aware Generative Frame Interpolation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guozhen Zhang, Kai Ma, Limin Wang, Xiaotong Zhao, Yuhan Zhu, Yutao Cui","submitted_at":"2025-01-07T11:03:43Z","abstract_excerpt":"Flow-based frame interpolation methods ensure motion stability through estimated intermediate flow but often introduce severe artifacts in complex motion regions. Recent generative approaches, boosted by large-scale pre-trained video generation models, show promise in handling intricate scenes. However, they frequently produce unstable motion and content inconsistencies due to the absence of explicit motion trajectory constraints. To address these challenges, we propose Motion-aware Generative frame interpolation (MoG) that synergizes intermediate flow guidance with generative capacities to en"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03699","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/2501.03699/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":"2501.03699","created_at":"2026-07-05T10:26:42.812891+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.03699v2","created_at":"2026-07-05T10:26:42.812891+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03699","created_at":"2026-07-05T10:26:42.812891+00:00"},{"alias_kind":"pith_short_12","alias_value":"TUR6IQMMR5AQ","created_at":"2026-07-05T10:26:42.812891+00:00"},{"alias_kind":"pith_short_16","alias_value":"TUR6IQMMR5AQRIOL","created_at":"2026-07-05T10:26:42.812891+00:00"},{"alias_kind":"pith_short_8","alias_value":"TUR6IQMM","created_at":"2026-07-05T10:26:42.812891+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02586","citing_title":"Fewer, Better Frames: A Compute-Normalized Proof of Concept for Coherence-First World-Model Rendering with Model-Guided FSR4 Frame Generation","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17749","citing_title":"Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO","json":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO.json","graph_json":"https://pith.science/api/pith-number/TUR6IQMMR5AQRIOLFFOKO7YHGO/graph.json","events_json":"https://pith.science/api/pith-number/TUR6IQMMR5AQRIOLFFOKO7YHGO/events.json","paper":"https://pith.science/paper/TUR6IQMM"},"agent_actions":{"view_html":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO","download_json":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO.json","view_paper":"https://pith.science/paper/TUR6IQMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.03699&json=true","fetch_graph":"https://pith.science/api/pith-number/TUR6IQMMR5AQRIOLFFOKO7YHGO/graph.json","fetch_events":"https://pith.science/api/pith-number/TUR6IQMMR5AQRIOLFFOKO7YHGO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO/action/storage_attestation","attest_author":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO/action/author_attestation","sign_citation":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO/action/citation_signature","submit_replication":"https://pith.science/pith/TUR6IQMMR5AQRIOLFFOKO7YHGO/action/replication_record"}},"created_at":"2026-07-05T10:26:42.812891+00:00","updated_at":"2026-07-05T10:26:42.812891+00:00"}