{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TQDSPDUZSJKIPVJWN47PGN7MRT","short_pith_number":"pith:TQDSPDUZ","schema_version":"1.0","canonical_sha256":"9c07278e99925487d5366f3ef337ec8cf5d4140858b666fb9369a73f4d50411c","source":{"kind":"arxiv","id":"1909.02749","version":1},"attestation_state":"computed","paper":{"title":"Video Interpolation and Prediction with Unsupervised Landmarks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Andrew Tao, Animesh Garg, Aysegul Dundar, Bryan Catanzaro, Kevin J. Shih, Robert Pottorf","submitted_at":"2019-09-06T07:40:27Z","abstract_excerpt":"Prediction and interpolation for long-range video data involves the complex task of modeling motion trajectories for each visible object, occlusions and dis-occlusions, as well as appearance changes due to viewpoint and lighting. Optical flow based techniques generalize but are suitable only for short temporal ranges. Many methods opt to project the video frames to a low dimensional latent space, achieving long-range predictions. However, these latent representations are often non-interpretable, and therefore difficult to manipulate. This work poses video prediction and interpolation as unsupe"},"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":"1909.02749","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-06T07:40:27Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"446ffa15f8d57b4be7aff9dda07d716b79c540ed5cf585f1770a41324f508eb2","abstract_canon_sha256":"de5d9b260d6175b2632ed50c3f3dfb730a07f744af39db924b7b82ad3d116296"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:42.430612Z","signature_b64":"nGWuXXLtqQGs/GCQIoAYSvx9Hj4UQKSVsWmwAEgERbmxRNsaIuHZCKQf7FDwa/y3hjiRrM9pzJKcuqUpWAMlCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c07278e99925487d5366f3ef337ec8cf5d4140858b666fb9369a73f4d50411c","last_reissued_at":"2026-07-05T00:02:42.430109Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:42.430109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Interpolation and Prediction with Unsupervised Landmarks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Andrew Tao, Animesh Garg, Aysegul Dundar, Bryan Catanzaro, Kevin J. Shih, Robert Pottorf","submitted_at":"2019-09-06T07:40:27Z","abstract_excerpt":"Prediction and interpolation for long-range video data involves the complex task of modeling motion trajectories for each visible object, occlusions and dis-occlusions, as well as appearance changes due to viewpoint and lighting. Optical flow based techniques generalize but are suitable only for short temporal ranges. Many methods opt to project the video frames to a low dimensional latent space, achieving long-range predictions. However, these latent representations are often non-interpretable, and therefore difficult to manipulate. This work poses video prediction and interpolation as unsupe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02749","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/1909.02749/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":"1909.02749","created_at":"2026-07-05T00:02:42.430174+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.02749v1","created_at":"2026-07-05T00:02:42.430174+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02749","created_at":"2026-07-05T00:02:42.430174+00:00"},{"alias_kind":"pith_short_12","alias_value":"TQDSPDUZSJKI","created_at":"2026-07-05T00:02:42.430174+00:00"},{"alias_kind":"pith_short_16","alias_value":"TQDSPDUZSJKIPVJW","created_at":"2026-07-05T00:02:42.430174+00:00"},{"alias_kind":"pith_short_8","alias_value":"TQDSPDUZ","created_at":"2026-07-05T00:02:42.430174+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/TQDSPDUZSJKIPVJWN47PGN7MRT","json":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT.json","graph_json":"https://pith.science/api/pith-number/TQDSPDUZSJKIPVJWN47PGN7MRT/graph.json","events_json":"https://pith.science/api/pith-number/TQDSPDUZSJKIPVJWN47PGN7MRT/events.json","paper":"https://pith.science/paper/TQDSPDUZ"},"agent_actions":{"view_html":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT","download_json":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT.json","view_paper":"https://pith.science/paper/TQDSPDUZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.02749&json=true","fetch_graph":"https://pith.science/api/pith-number/TQDSPDUZSJKIPVJWN47PGN7MRT/graph.json","fetch_events":"https://pith.science/api/pith-number/TQDSPDUZSJKIPVJWN47PGN7MRT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT/action/storage_attestation","attest_author":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT/action/author_attestation","sign_citation":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT/action/citation_signature","submit_replication":"https://pith.science/pith/TQDSPDUZSJKIPVJWN47PGN7MRT/action/replication_record"}},"created_at":"2026-07-05T00:02:42.430174+00:00","updated_at":"2026-07-05T00:02:42.430174+00:00"}