{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:FMBBJRDMLIDAT4NWU4LZQWD6ZX","short_pith_number":"pith:FMBBJRDM","canonical_record":{"source":{"id":"1405.1213","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T10:13:08Z","cross_cats_sorted":[],"title_canon_sha256":"37332fcbc43a8902010d70de13306fe3a3a7b485dad67d442e2c5244f3f40423","abstract_canon_sha256":"76e9e398c9620c00405852a771874b76786c438483bcef1755bc977a01c4de81"},"schema_version":"1.0"},"canonical_sha256":"2b0214c46c5a0609f1b6a71798587ecdcc374172541a88b16e80b06e20faf65b","source":{"kind":"arxiv","id":"1405.1213","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.1213","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"arxiv_version","alias_value":"1405.1213v2","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.1213","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"pith_short_12","alias_value":"FMBBJRDMLIDA","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FMBBJRDMLIDAT4NW","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FMBBJRDM","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:FMBBJRDMLIDAT4NWU4LZQWD6ZX","target":"record","payload":{"canonical_record":{"source":{"id":"1405.1213","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T10:13:08Z","cross_cats_sorted":[],"title_canon_sha256":"37332fcbc43a8902010d70de13306fe3a3a7b485dad67d442e2c5244f3f40423","abstract_canon_sha256":"76e9e398c9620c00405852a771874b76786c438483bcef1755bc977a01c4de81"},"schema_version":"1.0"},"canonical_sha256":"2b0214c46c5a0609f1b6a71798587ecdcc374172541a88b16e80b06e20faf65b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:00.554301Z","signature_b64":"Z3pwNSyF9K0rzK7+xAcdfh+72s2/wrJ1tcriCdXTMGzdnZnb77P1B93ApfwRul1dSkBSL2beE/8C6A+KkqqrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b0214c46c5a0609f1b6a71798587ecdcc374172541a88b16e80b06e20faf65b","last_reissued_at":"2026-05-18T02:51:00.553831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:00.553831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.1213","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":"2026-05-18T02:51:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+8m5fVI9uDT6MeTigfAXQz3lUKM6I474jfnjlJ08N9dd5E3/eJvdwpxF1oBPOoNztN/mB+lwsgaEW60U5w86CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:18:12.588467Z"},"content_sha256":"d499654d60fa3462081338e743fe5ccfd64ba8541319d008933384ffcefe904b","schema_version":"1.0","event_id":"sha256:d499654d60fa3462081338e743fe5ccfd64ba8541319d008933384ffcefe904b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:FMBBJRDMLIDAT4NWU4LZQWD6ZX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Human Pose Estimation from RGB Input Using Synthetic Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Omid Aghazadeh, Oscar Danielsson","submitted_at":"2014-05-06T10:13:08Z","abstract_excerpt":"We address the problem of estimating the pose of humans using RGB image input. More specifically, we are using a random forest classifier to classify pixels into joint-based body part categories, much similar to the famous Kinect pose estimator [11], [12]. However, we are using pure RGB input, i.e. no depth. Since the random forest requires a large number of training examples, we are using computer graphics generated, synthetic training data. In addition, we assume that we have access to a large number of real images with bounding box labels, extracted for example by a pedestrian detector or a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.1213","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":"2026-05-18T02:51:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7meZcOipaDJMk+f+0MctqMB79Uz2K4S5q7oKfHt00gPT9WyZdF6PEUAYLApUyeL4aRCDpQ220q69nBV7Ir79AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:18:12.589076Z"},"content_sha256":"963f2bd15d4d373d3d5ddd4083995afa29df6281a14fb34eeb5e5cf8f0cd6957","schema_version":"1.0","event_id":"sha256:963f2bd15d4d373d3d5ddd4083995afa29df6281a14fb34eeb5e5cf8f0cd6957"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/bundle.json","state_url":"https://pith.science/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/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":"2026-05-31T01:18:12Z","links":{"resolver":"https://pith.science/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX","bundle":"https://pith.science/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/bundle.json","state":"https://pith.science/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FMBBJRDMLIDAT4NWU4LZQWD6ZX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:FMBBJRDMLIDAT4NWU4LZQWD6ZX","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":"76e9e398c9620c00405852a771874b76786c438483bcef1755bc977a01c4de81","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T10:13:08Z","title_canon_sha256":"37332fcbc43a8902010d70de13306fe3a3a7b485dad67d442e2c5244f3f40423"},"schema_version":"1.0","source":{"id":"1405.1213","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.1213","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"arxiv_version","alias_value":"1405.1213v2","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.1213","created_at":"2026-05-18T02:51:00Z"},{"alias_kind":"pith_short_12","alias_value":"FMBBJRDMLIDA","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FMBBJRDMLIDAT4NW","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FMBBJRDM","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:963f2bd15d4d373d3d5ddd4083995afa29df6281a14fb34eeb5e5cf8f0cd6957","target":"graph","created_at":"2026-05-18T02:51:00Z","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":"We address the problem of estimating the pose of humans using RGB image input. More specifically, we are using a random forest classifier to classify pixels into joint-based body part categories, much similar to the famous Kinect pose estimator [11], [12]. However, we are using pure RGB input, i.e. no depth. Since the random forest requires a large number of training examples, we are using computer graphics generated, synthetic training data. In addition, we assume that we have access to a large number of real images with bounding box labels, extracted for example by a pedestrian detector or a","authors_text":"Omid Aghazadeh, Oscar Danielsson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T10:13:08Z","title":"Human Pose Estimation from RGB Input Using Synthetic Training Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.1213","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:d499654d60fa3462081338e743fe5ccfd64ba8541319d008933384ffcefe904b","target":"record","created_at":"2026-05-18T02:51:00Z","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":"76e9e398c9620c00405852a771874b76786c438483bcef1755bc977a01c4de81","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T10:13:08Z","title_canon_sha256":"37332fcbc43a8902010d70de13306fe3a3a7b485dad67d442e2c5244f3f40423"},"schema_version":"1.0","source":{"id":"1405.1213","kind":"arxiv","version":2}},"canonical_sha256":"2b0214c46c5a0609f1b6a71798587ecdcc374172541a88b16e80b06e20faf65b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b0214c46c5a0609f1b6a71798587ecdcc374172541a88b16e80b06e20faf65b","first_computed_at":"2026-05-18T02:51:00.553831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:51:00.553831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z3pwNSyF9K0rzK7+xAcdfh+72s2/wrJ1tcriCdXTMGzdnZnb77P1B93ApfwRul1dSkBSL2beE/8C6A+KkqqrDg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:51:00.554301Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.1213","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d499654d60fa3462081338e743fe5ccfd64ba8541319d008933384ffcefe904b","sha256:963f2bd15d4d373d3d5ddd4083995afa29df6281a14fb34eeb5e5cf8f0cd6957"],"state_sha256":"45f300741684dd3853b937c4f73fa4bf9b64b36871f2cd3cbe2c20aa98798abc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AJsInZKvO/OoPst+RwLjGd0juzC9WGGsTY4sAspudUGDKmA8HVTDJ3gZv4qz2zxgZ6RwMyZwuqxu/y2bIFo3Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T01:18:12.591532Z","bundle_sha256":"f9e126d85c95f8710d192a8d3b32d719b59e6972868e5444931561eba0824317"}}