{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:YFRGK6ZPOZC5SMKCJZPRV74B6D","short_pith_number":"pith:YFRGK6ZP","canonical_record":{"source":{"id":"1502.06807","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-24T13:39:55Z","cross_cats_sorted":[],"title_canon_sha256":"24b95b56bf23ac7ee20b95010ba8cfd6eabce0e3c1a28b05cde70f4fdbd8fca7","abstract_canon_sha256":"e58c747669a5534ceca72d0c48cced2803ffb8d4de601b4bb18eb7715bba8ca3"},"schema_version":"1.0"},"canonical_sha256":"c162657b2f7645d931424e5f1aff81f0ecccd0cfa32861d159583303bce0e037","source":{"kind":"arxiv","id":"1502.06807","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.06807","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"arxiv_version","alias_value":"1502.06807v2","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.06807","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"pith_short_12","alias_value":"YFRGK6ZPOZC5","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YFRGK6ZPOZC5SMKC","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YFRGK6ZP","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:YFRGK6ZPOZC5SMKCJZPRV74B6D","target":"record","payload":{"canonical_record":{"source":{"id":"1502.06807","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-24T13:39:55Z","cross_cats_sorted":[],"title_canon_sha256":"24b95b56bf23ac7ee20b95010ba8cfd6eabce0e3c1a28b05cde70f4fdbd8fca7","abstract_canon_sha256":"e58c747669a5534ceca72d0c48cced2803ffb8d4de601b4bb18eb7715bba8ca3"},"schema_version":"1.0"},"canonical_sha256":"c162657b2f7645d931424e5f1aff81f0ecccd0cfa32861d159583303bce0e037","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:04.745908Z","signature_b64":"c9UB/jIhpw90QZz5+zVxzI6f1DLq26O6CaMH93z6dmh7Vw6+y131VqZVXQYraU+NutJ0iNgcg/PISaZwgHq5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c162657b2f7645d931424e5f1aff81f0ecccd0cfa32861d159583303bce0e037","last_reissued_at":"2026-05-18T00:56:04.745342Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:04.745342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.06807","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":"2026-05-18T00:56:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LvR5nyxTpR+LzzZIrB/UFuZp3RuLBrb9EaW3lVoCnf7E9YGTp95TkHAXAHjz/dLMyrd6xPKvHnxWyGCg+9IyBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:53:57.817726Z"},"content_sha256":"eb53d6617de90db59f195ccc2f3872879798e97033cea17647cbba9acba3be3d","schema_version":"1.0","event_id":"sha256:eb53d6617de90db59f195ccc2f3872879798e97033cea17647cbba9acba3be3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:YFRGK6ZPOZC5SMKCJZPRV74B6D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hands Deep in Deep Learning for Hand Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Markus Oberweger, Paul Wohlhart, Vincent Lepetit","submitted_at":"2015-02-24T13:39:55Z","abstract_excerpt":"We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-the-art on several challenging benchmarks, both in terms of accuracy and computation times."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.06807","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":"2026-05-18T00:56:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"twkYV3JHxJBpI5gm8hfyyqihnOrl5gSXb4SDb5mmPAp5/GFD4qXqSQOD4pZ0jj51SeOwUWuE8SKJNAqsbxrDDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:53:57.818346Z"},"content_sha256":"174050e750487f4a417e0b417ce8e1e522c4e32bed01c074e64aa2a99f465299","schema_version":"1.0","event_id":"sha256:174050e750487f4a417e0b417ce8e1e522c4e32bed01c074e64aa2a99f465299"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/bundle.json","state_url":"https://pith.science/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/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":"2026-05-25T22:53:57Z","links":{"resolver":"https://pith.science/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D","bundle":"https://pith.science/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/bundle.json","state":"https://pith.science/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFRGK6ZPOZC5SMKCJZPRV74B6D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:YFRGK6ZPOZC5SMKCJZPRV74B6D","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":"e58c747669a5534ceca72d0c48cced2803ffb8d4de601b4bb18eb7715bba8ca3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-24T13:39:55Z","title_canon_sha256":"24b95b56bf23ac7ee20b95010ba8cfd6eabce0e3c1a28b05cde70f4fdbd8fca7"},"schema_version":"1.0","source":{"id":"1502.06807","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.06807","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"arxiv_version","alias_value":"1502.06807v2","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.06807","created_at":"2026-05-18T00:56:04Z"},{"alias_kind":"pith_short_12","alias_value":"YFRGK6ZPOZC5","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YFRGK6ZPOZC5SMKC","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YFRGK6ZP","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:174050e750487f4a417e0b417ce8e1e522c4e32bed01c074e64aa2a99f465299","target":"graph","created_at":"2026-05-18T00:56:04Z","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 introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-the-art on several challenging benchmarks, both in terms of accuracy and computation times.","authors_text":"Markus Oberweger, Paul Wohlhart, Vincent Lepetit","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-24T13:39:55Z","title":"Hands Deep in Deep Learning for Hand Pose Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.06807","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:eb53d6617de90db59f195ccc2f3872879798e97033cea17647cbba9acba3be3d","target":"record","created_at":"2026-05-18T00:56:04Z","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":"e58c747669a5534ceca72d0c48cced2803ffb8d4de601b4bb18eb7715bba8ca3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-24T13:39:55Z","title_canon_sha256":"24b95b56bf23ac7ee20b95010ba8cfd6eabce0e3c1a28b05cde70f4fdbd8fca7"},"schema_version":"1.0","source":{"id":"1502.06807","kind":"arxiv","version":2}},"canonical_sha256":"c162657b2f7645d931424e5f1aff81f0ecccd0cfa32861d159583303bce0e037","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c162657b2f7645d931424e5f1aff81f0ecccd0cfa32861d159583303bce0e037","first_computed_at":"2026-05-18T00:56:04.745342Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:56:04.745342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c9UB/jIhpw90QZz5+zVxzI6f1DLq26O6CaMH93z6dmh7Vw6+y131VqZVXQYraU+NutJ0iNgcg/PISaZwgHq5BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:56:04.745908Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.06807","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb53d6617de90db59f195ccc2f3872879798e97033cea17647cbba9acba3be3d","sha256:174050e750487f4a417e0b417ce8e1e522c4e32bed01c074e64aa2a99f465299"],"state_sha256":"9e25da284285c8fd6daa8c423b90d6d2084887e9f96829a40f8165bb402dc907"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mjVPeZ1bMZBWFzYB3n+l5mFJatXnwTHjv9MVYgYwOd5p4XSzbdqSL9evPAdZ7kQcvY6626lDgWI7Ifm+mbHTAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:53:57.821474Z","bundle_sha256":"a9a19dcc1af4d3a8494d09a80b8eadc7ff83096ddeef6a408725e5c7804c2c26"}}