{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:7BRRHIXWTCLXRQMTNE6LMD7L34","short_pith_number":"pith:7BRRHIXW","canonical_record":{"source":{"id":"1612.00881","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-02T22:24:24Z","cross_cats_sorted":[],"title_canon_sha256":"ff542e475cba2487a898c16d6cd299a6d27d328ce08114c799036d7464dc2f5d","abstract_canon_sha256":"5065386f5714baa726d94225763eab13290b056b5cbd172a71451d6aed990235"},"schema_version":"1.0"},"canonical_sha256":"f86313a2f6989778c193693cb60febdf0f4d82d6645b2fec1a380caa2fe2886f","source":{"kind":"arxiv","id":"1612.00881","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.00881","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.00881v2","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.00881","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"pith_short_12","alias_value":"7BRRHIXWTCLX","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7BRRHIXWTCLXRQMT","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7BRRHIXW","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:7BRRHIXWTCLXRQMTNE6LMD7L34","target":"record","payload":{"canonical_record":{"source":{"id":"1612.00881","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-02T22:24:24Z","cross_cats_sorted":[],"title_canon_sha256":"ff542e475cba2487a898c16d6cd299a6d27d328ce08114c799036d7464dc2f5d","abstract_canon_sha256":"5065386f5714baa726d94225763eab13290b056b5cbd172a71451d6aed990235"},"schema_version":"1.0"},"canonical_sha256":"f86313a2f6989778c193693cb60febdf0f4d82d6645b2fec1a380caa2fe2886f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:58.327473Z","signature_b64":"LQjPzJNXYhWFmXTRazAuPF9uOadfK6xMmfo43TvNiIcmHbNdPujezQO0JSmYgwbcUrHbmIaC52cI5i45KA3vBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f86313a2f6989778c193693cb60febdf0f4d82d6645b2fec1a380caa2fe2886f","last_reissued_at":"2026-05-18T00:39:58.326990Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:58.326990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.00881","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FgSHPH1/WBVv0QdTbsibVcYcMUvFRJP3oXZRb+unt1TDR18avpJk9retD1cwf5FR0utz8phETyZtG6ajiq/ECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:13:48.406225Z"},"content_sha256":"843bfe3f7ae705e8bf1cdda167dd42a3fb0ee08d9f3a3ae3d31aef5738defcfa","schema_version":"1.0","event_id":"sha256:843bfe3f7ae705e8bf1cdda167dd42a3fb0ee08d9f3a3ae3d31aef5738defcfa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:7BRRHIXWTCLXRQMTNE6LMD7L34","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Procedural Generation of Videos to Train Deep Action Recognition Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Antonio Manuel L\\'opez Pe\\~na, C\\'esar Roberto de Souza, Yohann Cabon","submitted_at":"2016-12-02T22:24:24Z","abstract_excerpt":"Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.00881","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":""},"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":"2026-05-18T00:39:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wS7SRZZPKyfV/1tBFv4iaPzsdTPQx54vhnlI449hc3ksNW40lJ9pM4XdBBU/ZF/jAWY1sF2/VjQkP2cOM2z6BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:13:48.406580Z"},"content_sha256":"a9d9e9dce1a228b6635c99545e453d2c786ba5b2662f48464f1026b19f15981b","schema_version":"1.0","event_id":"sha256:a9d9e9dce1a228b6635c99545e453d2c786ba5b2662f48464f1026b19f15981b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/bundle.json","state_url":"https://pith.science/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/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":"2026-06-04T01:13:48Z","links":{"resolver":"https://pith.science/pith/7BRRHIXWTCLXRQMTNE6LMD7L34","bundle":"https://pith.science/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/bundle.json","state":"https://pith.science/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7BRRHIXWTCLXRQMTNE6LMD7L34/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:7BRRHIXWTCLXRQMTNE6LMD7L34","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":"5065386f5714baa726d94225763eab13290b056b5cbd172a71451d6aed990235","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-02T22:24:24Z","title_canon_sha256":"ff542e475cba2487a898c16d6cd299a6d27d328ce08114c799036d7464dc2f5d"},"schema_version":"1.0","source":{"id":"1612.00881","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.00881","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.00881v2","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.00881","created_at":"2026-05-18T00:39:58Z"},{"alias_kind":"pith_short_12","alias_value":"7BRRHIXWTCLX","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7BRRHIXWTCLXRQMT","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7BRRHIXW","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:a9d9e9dce1a228b6635c99545e453d2c786ba5b2662f48464f1026b19f15981b","target":"graph","created_at":"2026-05-18T00:39:58Z","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":"Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible","authors_text":"Adrien Gaidon, Antonio Manuel L\\'opez Pe\\~na, C\\'esar Roberto de Souza, Yohann Cabon","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-02T22:24:24Z","title":"Procedural Generation of Videos to Train Deep Action Recognition Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.00881","kind":"arxiv","version":2},"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":"sha256:843bfe3f7ae705e8bf1cdda167dd42a3fb0ee08d9f3a3ae3d31aef5738defcfa","target":"record","created_at":"2026-05-18T00:39:58Z","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":"5065386f5714baa726d94225763eab13290b056b5cbd172a71451d6aed990235","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-02T22:24:24Z","title_canon_sha256":"ff542e475cba2487a898c16d6cd299a6d27d328ce08114c799036d7464dc2f5d"},"schema_version":"1.0","source":{"id":"1612.00881","kind":"arxiv","version":2}},"canonical_sha256":"f86313a2f6989778c193693cb60febdf0f4d82d6645b2fec1a380caa2fe2886f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f86313a2f6989778c193693cb60febdf0f4d82d6645b2fec1a380caa2fe2886f","first_computed_at":"2026-05-18T00:39:58.326990Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:58.326990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LQjPzJNXYhWFmXTRazAuPF9uOadfK6xMmfo43TvNiIcmHbNdPujezQO0JSmYgwbcUrHbmIaC52cI5i45KA3vBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:58.327473Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.00881","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:843bfe3f7ae705e8bf1cdda167dd42a3fb0ee08d9f3a3ae3d31aef5738defcfa","sha256:a9d9e9dce1a228b6635c99545e453d2c786ba5b2662f48464f1026b19f15981b"],"state_sha256":"5de7d71e11669ccee72b9ece8427308403e236196d495aff49c0362afed9c445"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BteNlPNqxetJ+A2a+QYyM8TIRcqBMEwcU1xr2B3PqOtjxKuIiYKCpVt72AUboS+X/2VvRWH7CSpbW32m3WGCCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T01:13:48.408594Z","bundle_sha256":"16e597d3c2d90e7a955f2d5a50d8fa617da237ddd33636213509f94cab8c943c"}}