{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:VXLEENMPCCH26CV6KB2IVY2TOG","short_pith_number":"pith:VXLEENMP","canonical_record":{"source":{"id":"2102.05822","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-11T02:59:55Z","cross_cats_sorted":[],"title_canon_sha256":"347b363ebeda874c95b5211aee6a3a00ac722f1dfcfd00a07062d29c65f50eb5","abstract_canon_sha256":"0dadd4170b25891a80c827b34c2ccd9e7cebdb0db70bb8915208411e07227b9a"},"schema_version":"1.0"},"canonical_sha256":"add642358f108faf0abe50748ae35371adf4fda21ecf60549262bf9029c91d0e","source":{"kind":"arxiv","id":"2102.05822","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.05822","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.05822v1","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.05822","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_12","alias_value":"VXLEENMPCCH2","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_16","alias_value":"VXLEENMPCCH26CV6","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_8","alias_value":"VXLEENMP","created_at":"2026-07-05T02:14:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:VXLEENMPCCH26CV6KB2IVY2TOG","target":"record","payload":{"canonical_record":{"source":{"id":"2102.05822","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-11T02:59:55Z","cross_cats_sorted":[],"title_canon_sha256":"347b363ebeda874c95b5211aee6a3a00ac722f1dfcfd00a07062d29c65f50eb5","abstract_canon_sha256":"0dadd4170b25891a80c827b34c2ccd9e7cebdb0db70bb8915208411e07227b9a"},"schema_version":"1.0"},"canonical_sha256":"add642358f108faf0abe50748ae35371adf4fda21ecf60549262bf9029c91d0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:14:34.921464Z","signature_b64":"q0NAhebZwNrPggHog5RqMBr0LE3Rm9Xfi5nPWy52kWG66rcbpVZaSlthi6RuFKP/9BIrh9TbCT8EteNMFv+LDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"add642358f108faf0abe50748ae35371adf4fda21ecf60549262bf9029c91d0e","last_reissued_at":"2026-07-05T02:14:34.921058Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:14:34.921058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.05822","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":"2026-07-05T02:14:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sgJRsIpqsj3wCriwTl5w3AOfcKt+3zpHHiayg90CCDuAtwoYKX38/+XOhz2hhr3jA/5Ip6IYhA+lP0YUhiyUCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:01:24.403434Z"},"content_sha256":"16f0135dc10ad1d2dbb21d44704b5c64644571242301eaac398e2001304964fe","schema_version":"1.0","event_id":"sha256:16f0135dc10ad1d2dbb21d44704b5c64644571242301eaac398e2001304964fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:VXLEENMPCCH26CV6KB2IVY2TOG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Frame Difference-Based Temporal Loss for Video Stylization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianjin Xu, Xiaolin Hu, Zheyang Xiong","submitted_at":"2021-02-11T02:59:55Z","abstract_excerpt":"Neural style transfer models have been used to stylize an ordinary video to specific styles. To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow. This is achieved by minimizing an optical flow-based (OFB) loss during model training. However, optical flow estimation is itself a challenging task, particularly in complex scenes. In addition, it incurs a high computational cost. We propose a much simpler temporal loss called the f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.05822","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/2102.05822/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:14:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+5zTa7h9kWLj0enAmxj7WoPCXFUBjbKO17PQr+IX24RFEJmGOu38Qt1698pm3jazKXMlHrokJQOcgeGHlXa6AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:01:24.403814Z"},"content_sha256":"d88cac45c373af41df7839f2e41aba3bffe5046b941d15b30612365738856aa0","schema_version":"1.0","event_id":"sha256:d88cac45c373af41df7839f2e41aba3bffe5046b941d15b30612365738856aa0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VXLEENMPCCH26CV6KB2IVY2TOG/bundle.json","state_url":"https://pith.science/pith/VXLEENMPCCH26CV6KB2IVY2TOG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VXLEENMPCCH26CV6KB2IVY2TOG/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":"2026-07-09T02:01:24Z","links":{"resolver":"https://pith.science/pith/VXLEENMPCCH26CV6KB2IVY2TOG","bundle":"https://pith.science/pith/VXLEENMPCCH26CV6KB2IVY2TOG/bundle.json","state":"https://pith.science/pith/VXLEENMPCCH26CV6KB2IVY2TOG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VXLEENMPCCH26CV6KB2IVY2TOG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:VXLEENMPCCH26CV6KB2IVY2TOG","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":"0dadd4170b25891a80c827b34c2ccd9e7cebdb0db70bb8915208411e07227b9a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-11T02:59:55Z","title_canon_sha256":"347b363ebeda874c95b5211aee6a3a00ac722f1dfcfd00a07062d29c65f50eb5"},"schema_version":"1.0","source":{"id":"2102.05822","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.05822","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.05822v1","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.05822","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_12","alias_value":"VXLEENMPCCH2","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_16","alias_value":"VXLEENMPCCH26CV6","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_8","alias_value":"VXLEENMP","created_at":"2026-07-05T02:14:34Z"}],"graph_snapshots":[{"event_id":"sha256:d88cac45c373af41df7839f2e41aba3bffe5046b941d15b30612365738856aa0","target":"graph","created_at":"2026-07-05T02:14:34Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2102.05822/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural style transfer models have been used to stylize an ordinary video to specific styles. To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow. This is achieved by minimizing an optical flow-based (OFB) loss during model training. However, optical flow estimation is itself a challenging task, particularly in complex scenes. In addition, it incurs a high computational cost. We propose a much simpler temporal loss called the f","authors_text":"Jianjin Xu, Xiaolin Hu, Zheyang Xiong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-11T02:59:55Z","title":"Frame Difference-Based Temporal Loss for Video Stylization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.05822","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":"sha256:16f0135dc10ad1d2dbb21d44704b5c64644571242301eaac398e2001304964fe","target":"record","created_at":"2026-07-05T02:14:34Z","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":"0dadd4170b25891a80c827b34c2ccd9e7cebdb0db70bb8915208411e07227b9a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-11T02:59:55Z","title_canon_sha256":"347b363ebeda874c95b5211aee6a3a00ac722f1dfcfd00a07062d29c65f50eb5"},"schema_version":"1.0","source":{"id":"2102.05822","kind":"arxiv","version":1}},"canonical_sha256":"add642358f108faf0abe50748ae35371adf4fda21ecf60549262bf9029c91d0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"add642358f108faf0abe50748ae35371adf4fda21ecf60549262bf9029c91d0e","first_computed_at":"2026-07-05T02:14:34.921058Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:14:34.921058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q0NAhebZwNrPggHog5RqMBr0LE3Rm9Xfi5nPWy52kWG66rcbpVZaSlthi6RuFKP/9BIrh9TbCT8EteNMFv+LDg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:14:34.921464Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.05822","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16f0135dc10ad1d2dbb21d44704b5c64644571242301eaac398e2001304964fe","sha256:d88cac45c373af41df7839f2e41aba3bffe5046b941d15b30612365738856aa0"],"state_sha256":"5ef6c33320e37c786dfccca0b93da814e44242978a5a2e5eb1a59fe991750259"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yKEUHArfBStkvXIWHyTIGqzbffDNCy8LLaQvV+ragxC0pEuTbIWJp2gBhdoctXuRrf/bcXCFgC4VQDI2Z++1Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T02:01:24.405834Z","bundle_sha256":"5418c202c7b4c8e8b0c2e0443d244a19bed21a2404f7b277815794b12a254646"}}