{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:EKVBD4PENDDVJC5A2MQBFO6NQL","short_pith_number":"pith:EKVBD4PE","canonical_record":{"source":{"id":"2504.12165","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T15:18:11Z","cross_cats_sorted":[],"title_canon_sha256":"a9ed3120185dc1601fadb43ff98be941b817da62823b409eb51c485997b2d094","abstract_canon_sha256":"79523a5055990eccde06002c669e993d3e2dfa3d9ddcab605c7f3ca94dd6c2a7"},"schema_version":"1.0"},"canonical_sha256":"22aa11f1e468c7548ba0d32012bbcd82f84c8b97075a6ebab5c07864bf93db2e","source":{"kind":"arxiv","id":"2504.12165","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12165","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12165v1","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12165","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_12","alias_value":"EKVBD4PENDDV","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_16","alias_value":"EKVBD4PENDDVJC5A","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_8","alias_value":"EKVBD4PE","created_at":"2026-07-05T10:50:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:EKVBD4PENDDVJC5A2MQBFO6NQL","target":"record","payload":{"canonical_record":{"source":{"id":"2504.12165","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T15:18:11Z","cross_cats_sorted":[],"title_canon_sha256":"a9ed3120185dc1601fadb43ff98be941b817da62823b409eb51c485997b2d094","abstract_canon_sha256":"79523a5055990eccde06002c669e993d3e2dfa3d9ddcab605c7f3ca94dd6c2a7"},"schema_version":"1.0"},"canonical_sha256":"22aa11f1e468c7548ba0d32012bbcd82f84c8b97075a6ebab5c07864bf93db2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:03.594410Z","signature_b64":"0Qfd2FjcC/8Je9/wPIHvAxS//Mudd0vpstkbgOz1ujbe4hKFBmWSJArAnVtHAVdgouR/X3wvYKkK9i0rx9LfBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22aa11f1e468c7548ba0d32012bbcd82f84c8b97075a6ebab5c07864bf93db2e","last_reissued_at":"2026-07-05T10:50:03.593866Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:03.593866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.12165","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":"2026-07-05T10:50:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vH7sxwj7n5EYYCu52KpV4VwE96HoND7ci1+0ha0M+h+XWjJVNpcGrLnUZSkdjXkD1OIhx9z3sLud2MRmALetCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:42:21.003861Z"},"content_sha256":"e0aae47c39fa9307ce2189a486a10d8af7d3ff54b0725435655f73b418ea1eaa","schema_version":"1.0","event_id":"sha256:e0aae47c39fa9307ce2189a486a10d8af7d3ff54b0725435655f73b418ea1eaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:EKVBD4PENDDVJC5A2MQBFO6NQL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CodingHomo: Bootstrapping Deep Homography With Video Coding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Zeng, Haipeng Li, Shuaicheng Liu, Yike Liu","submitted_at":"2025-04-16T15:18:11Z","abstract_excerpt":"Homography estimation is a fundamental task in computer vision with applications in diverse fields. Recent advances in deep learning have improved homography estimation, particularly with unsupervised learning approaches, offering increased robustness and generalizability. However, accurately predicting homography, especially in complex motions, remains a challenge. In response, this work introduces a novel method leveraging video coding, particularly by harnessing inherent motion vectors (MVs) present in videos. We present CodingHomo, an unsupervised framework for homography estimation. Our f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12165","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/2504.12165/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":"2026-07-05T10:50:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fcxDASzkqaJ7eJmCeVI7dLFRgqhbQXINLtRiKCKA6D6vccDXZJUrEn45CXTsZqvkjlQRWVhnEL7Zn58o1B0pCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:42:21.004383Z"},"content_sha256":"77f563bbefc6d2572412339c5a341cb2f6d283cd84b1caa7e1bd71a15c4aa825","schema_version":"1.0","event_id":"sha256:77f563bbefc6d2572412339c5a341cb2f6d283cd84b1caa7e1bd71a15c4aa825"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/bundle.json","state_url":"https://pith.science/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/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":"2026-08-21T02:42:21Z","links":{"resolver":"https://pith.science/pith/EKVBD4PENDDVJC5A2MQBFO6NQL","bundle":"https://pith.science/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/bundle.json","state":"https://pith.science/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EKVBD4PENDDVJC5A2MQBFO6NQL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EKVBD4PENDDVJC5A2MQBFO6NQL","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":"79523a5055990eccde06002c669e993d3e2dfa3d9ddcab605c7f3ca94dd6c2a7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T15:18:11Z","title_canon_sha256":"a9ed3120185dc1601fadb43ff98be941b817da62823b409eb51c485997b2d094"},"schema_version":"1.0","source":{"id":"2504.12165","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12165","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12165v1","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12165","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_12","alias_value":"EKVBD4PENDDV","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_16","alias_value":"EKVBD4PENDDVJC5A","created_at":"2026-07-05T10:50:03Z"},{"alias_kind":"pith_short_8","alias_value":"EKVBD4PE","created_at":"2026-07-05T10:50:03Z"}],"graph_snapshots":[{"event_id":"sha256:77f563bbefc6d2572412339c5a341cb2f6d283cd84b1caa7e1bd71a15c4aa825","target":"graph","created_at":"2026-07-05T10:50:03Z","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/2504.12165/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Homography estimation is a fundamental task in computer vision with applications in diverse fields. Recent advances in deep learning have improved homography estimation, particularly with unsupervised learning approaches, offering increased robustness and generalizability. However, accurately predicting homography, especially in complex motions, remains a challenge. In response, this work introduces a novel method leveraging video coding, particularly by harnessing inherent motion vectors (MVs) present in videos. We present CodingHomo, an unsupervised framework for homography estimation. Our f","authors_text":"Bing Zeng, Haipeng Li, Shuaicheng Liu, Yike Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T15:18:11Z","title":"CodingHomo: Bootstrapping Deep Homography With Video Coding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12165","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:e0aae47c39fa9307ce2189a486a10d8af7d3ff54b0725435655f73b418ea1eaa","target":"record","created_at":"2026-07-05T10:50:03Z","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":"79523a5055990eccde06002c669e993d3e2dfa3d9ddcab605c7f3ca94dd6c2a7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T15:18:11Z","title_canon_sha256":"a9ed3120185dc1601fadb43ff98be941b817da62823b409eb51c485997b2d094"},"schema_version":"1.0","source":{"id":"2504.12165","kind":"arxiv","version":1}},"canonical_sha256":"22aa11f1e468c7548ba0d32012bbcd82f84c8b97075a6ebab5c07864bf93db2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22aa11f1e468c7548ba0d32012bbcd82f84c8b97075a6ebab5c07864bf93db2e","first_computed_at":"2026-07-05T10:50:03.593866Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:03.593866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0Qfd2FjcC/8Je9/wPIHvAxS//Mudd0vpstkbgOz1ujbe4hKFBmWSJArAnVtHAVdgouR/X3wvYKkK9i0rx9LfBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:03.594410Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12165","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0aae47c39fa9307ce2189a486a10d8af7d3ff54b0725435655f73b418ea1eaa","sha256:77f563bbefc6d2572412339c5a341cb2f6d283cd84b1caa7e1bd71a15c4aa825"],"state_sha256":"ad4a006a26bf8b4199eba35b9fc5c550c4467e554ece573aa65f737e3b6e1c60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g1kWVp8ekAE1iNXKcgB2h3RcGrkOQp1ww5Mtc8sUJbXnKDVk3OA8ceLJDreXc4oEQ6kfFFlvnNKQzmxHahCDAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T02:42:21.010337Z","bundle_sha256":"2013e99df14a089be95fcad7387066cb18e85bd2d95268db0319e703dca66913"}}