{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TK457773SMH4U7TEBGIMAZMZ3U","short_pith_number":"pith:TK457773","canonical_record":{"source":{"id":"1805.12243","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-30T22:11:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4e1cd83da83280eb6c8cfcbeced57b51cc69f6ac088766fc5dca711b09b6bf58","abstract_canon_sha256":"65335c89a4cf1026d945a170d6875a5fb1a8580fa3b4bc0f8646d3e550de66fe"},"schema_version":"1.0"},"canonical_sha256":"9ab9dffffb930fca7e640990c06599dd3bd53d1f1831e95b9358563775993f35","source":{"kind":"arxiv","id":"1805.12243","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.12243","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"arxiv_version","alias_value":"1805.12243v1","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12243","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"pith_short_12","alias_value":"TK457773SMH4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TK457773SMH4U7TE","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TK457773","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TK457773SMH4U7TEBGIMAZMZ3U","target":"record","payload":{"canonical_record":{"source":{"id":"1805.12243","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-30T22:11:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4e1cd83da83280eb6c8cfcbeced57b51cc69f6ac088766fc5dca711b09b6bf58","abstract_canon_sha256":"65335c89a4cf1026d945a170d6875a5fb1a8580fa3b4bc0f8646d3e550de66fe"},"schema_version":"1.0"},"canonical_sha256":"9ab9dffffb930fca7e640990c06599dd3bd53d1f1831e95b9358563775993f35","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:30.988917Z","signature_b64":"LoPLd8J5sDvaI9PiRWg5B2qkk5sP1nz1gSJyOUMQdJs7XvWBJLQx9EGDw3iZTJ32iRqdjIQIgqCKvjeU5tCQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ab9dffffb930fca7e640990c06599dd3bd53d1f1831e95b9358563775993f35","last_reissued_at":"2026-05-18T00:14:30.988281Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:30.988281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.12243","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkDDdTkLT07VpChnDFBNApfPR91AeIBmndNp2emB2A8J4ZqwHDW8+ssVUNWwhGUSLvzS0YKe2d+62s0T2cJmBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T20:04:59.377771Z"},"content_sha256":"df19c007447a571ea828d38b704022a8a30960f82d58e93af5be023e61c229a2","schema_version":"1.0","event_id":"sha256:df19c007447a571ea828d38b704022a8a30960f82d58e93af5be023e61c229a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TK457773SMH4U7TEBGIMAZMZ3U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Novel Video Prediction for Large-scale Scene using Optical Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Henglai Wei, Penghong lin, Xiaochuan Yin","submitted_at":"2018-05-30T22:11:54Z","abstract_excerpt":"Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12243","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oREvj9hxhS9RC7VKxisufBAAbxrP1zhHn+cWZWNJFjFeiYf0hWkGBAwW0u7fYWC+xUAHyuVP2A+k1HsXPoYBCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T20:04:59.378111Z"},"content_sha256":"61afe2875afe5008d1bfd41e4e59775657c37ef6f6010a24e1d77ef812ec5868","schema_version":"1.0","event_id":"sha256:61afe2875afe5008d1bfd41e4e59775657c37ef6f6010a24e1d77ef812ec5868"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TK457773SMH4U7TEBGIMAZMZ3U/bundle.json","state_url":"https://pith.science/pith/TK457773SMH4U7TEBGIMAZMZ3U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TK457773SMH4U7TEBGIMAZMZ3U/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":"2026-06-29T20:04:59Z","links":{"resolver":"https://pith.science/pith/TK457773SMH4U7TEBGIMAZMZ3U","bundle":"https://pith.science/pith/TK457773SMH4U7TEBGIMAZMZ3U/bundle.json","state":"https://pith.science/pith/TK457773SMH4U7TEBGIMAZMZ3U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TK457773SMH4U7TEBGIMAZMZ3U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TK457773SMH4U7TEBGIMAZMZ3U","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":"65335c89a4cf1026d945a170d6875a5fb1a8580fa3b4bc0f8646d3e550de66fe","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-30T22:11:54Z","title_canon_sha256":"4e1cd83da83280eb6c8cfcbeced57b51cc69f6ac088766fc5dca711b09b6bf58"},"schema_version":"1.0","source":{"id":"1805.12243","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.12243","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"arxiv_version","alias_value":"1805.12243v1","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12243","created_at":"2026-05-18T00:14:30Z"},{"alias_kind":"pith_short_12","alias_value":"TK457773SMH4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TK457773SMH4U7TE","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TK457773","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:61afe2875afe5008d1bfd41e4e59775657c37ef6f6010a24e1d77ef812ec5868","target":"graph","created_at":"2026-05-18T00:14:30Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of t","authors_text":"Henglai Wei, Penghong lin, Xiaochuan Yin","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-30T22:11:54Z","title":"Novel Video Prediction for Large-scale Scene using Optical Flow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12243","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:df19c007447a571ea828d38b704022a8a30960f82d58e93af5be023e61c229a2","target":"record","created_at":"2026-05-18T00:14:30Z","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":"65335c89a4cf1026d945a170d6875a5fb1a8580fa3b4bc0f8646d3e550de66fe","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-30T22:11:54Z","title_canon_sha256":"4e1cd83da83280eb6c8cfcbeced57b51cc69f6ac088766fc5dca711b09b6bf58"},"schema_version":"1.0","source":{"id":"1805.12243","kind":"arxiv","version":1}},"canonical_sha256":"9ab9dffffb930fca7e640990c06599dd3bd53d1f1831e95b9358563775993f35","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ab9dffffb930fca7e640990c06599dd3bd53d1f1831e95b9358563775993f35","first_computed_at":"2026-05-18T00:14:30.988281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:30.988281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LoPLd8J5sDvaI9PiRWg5B2qkk5sP1nz1gSJyOUMQdJs7XvWBJLQx9EGDw3iZTJ32iRqdjIQIgqCKvjeU5tCQCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:30.988917Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.12243","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df19c007447a571ea828d38b704022a8a30960f82d58e93af5be023e61c229a2","sha256:61afe2875afe5008d1bfd41e4e59775657c37ef6f6010a24e1d77ef812ec5868"],"state_sha256":"eac36ceefc76866daea052fe34130704c62f84cd5b8d8214fc475f480f60eee5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Szj9ZZ7yWdl2bk5tMviY51o5k7V/d71bxvY8BuwzwXZTaW56nzD2Pm9OChQ1gtWUunI0EI+yaPL5Mdx1IekdDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T20:04:59.379995Z","bundle_sha256":"636f84c9fab925f52630be258842bd5a669cd59fc1408546e13a5009bb4433c9"}}