{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KV5J5M6ONASRGJ3H55O6V5BDCJ","short_pith_number":"pith:KV5J5M6O","canonical_record":{"source":{"id":"1706.04758","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-15T07:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"70351c627303cd9fb2b1b4b05fe80196daae7a8601a9eccc79359d13a6249902","abstract_canon_sha256":"9ab1ad0250c06d302c6ac4862bb4fdf425408c1ad08324102e8ac0b49920aa8c"},"schema_version":"1.0"},"canonical_sha256":"557a9eb3ce6825132767ef5deaf42312411237b332bafccfe786711f8d25786d","source":{"kind":"arxiv","id":"1706.04758","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04758","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04758v2","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04758","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"pith_short_12","alias_value":"KV5J5M6ONASR","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KV5J5M6ONASRGJ3H","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KV5J5M6O","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KV5J5M6ONASRGJ3H55O6V5BDCJ","target":"record","payload":{"canonical_record":{"source":{"id":"1706.04758","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-15T07:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"70351c627303cd9fb2b1b4b05fe80196daae7a8601a9eccc79359d13a6249902","abstract_canon_sha256":"9ab1ad0250c06d302c6ac4862bb4fdf425408c1ad08324102e8ac0b49920aa8c"},"schema_version":"1.0"},"canonical_sha256":"557a9eb3ce6825132767ef5deaf42312411237b332bafccfe786711f8d25786d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:39.867101Z","signature_b64":"jMwN6BY4eG59kxdoaAateZ7eMPxEAB1CBIndWLJaX//HCvBtW/tROoLKOatxIJBzxMyDG4L+0+kKwOwGiSqnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"557a9eb3ce6825132767ef5deaf42312411237b332bafccfe786711f8d25786d","last_reissued_at":"2026-05-18T00:40:39.866395Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:39.866395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.04758","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":"2026-05-18T00:40:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sO0lJNJd7bD4deuCUrSzcRL6qrjmEZ2FEXtSucy0vydBlQuaKDP47fNvhoIVA5JAMsS8ABrfLjNdHHxY5QwSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:46:00.974917Z"},"content_sha256":"9b324d519143521bbf6ccf800e4793e77b4ec29142d61636ce9f1fb0f10fd85e","schema_version":"1.0","event_id":"sha256:9b324d519143521bbf6ccf800e4793e77b4ec29142d61636ce9f1fb0f10fd85e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KV5J5M6ONASRGJ3H55O6V5BDCJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee, Yumin Suh","submitted_at":"2017-06-15T07:25:34Z","abstract_excerpt":"We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04758","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":"2026-05-18T00:40:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HvUDlzI634IHHB3hViE0JhDHvFYpZmAT9ePZ1jtPuTj8WGq6z+aWU7P9WKnvga6N0qrpW4Yh7O0wlKRui+4tDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:46:00.975400Z"},"content_sha256":"ef823f56d88121838b6d18a522dcdf1c7c7fcb4bd414bf08252f6490d8df92ee","schema_version":"1.0","event_id":"sha256:ef823f56d88121838b6d18a522dcdf1c7c7fcb4bd414bf08252f6490d8df92ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/bundle.json","state_url":"https://pith.science/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/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":"2026-05-25T23:46:00Z","links":{"resolver":"https://pith.science/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ","bundle":"https://pith.science/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/bundle.json","state":"https://pith.science/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KV5J5M6ONASRGJ3H55O6V5BDCJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KV5J5M6ONASRGJ3H55O6V5BDCJ","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":"9ab1ad0250c06d302c6ac4862bb4fdf425408c1ad08324102e8ac0b49920aa8c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-15T07:25:34Z","title_canon_sha256":"70351c627303cd9fb2b1b4b05fe80196daae7a8601a9eccc79359d13a6249902"},"schema_version":"1.0","source":{"id":"1706.04758","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04758","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04758v2","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04758","created_at":"2026-05-18T00:40:39Z"},{"alias_kind":"pith_short_12","alias_value":"KV5J5M6ONASR","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KV5J5M6ONASRGJ3H","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KV5J5M6O","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:ef823f56d88121838b6d18a522dcdf1c7c7fcb4bd414bf08252f6490d8df92ee","target":"graph","created_at":"2026-05-18T00:40:39Z","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 propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistent","authors_text":"Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee, Yumin Suh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-15T07:25:34Z","title":"Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04758","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":"sha256:9b324d519143521bbf6ccf800e4793e77b4ec29142d61636ce9f1fb0f10fd85e","target":"record","created_at":"2026-05-18T00:40:39Z","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":"9ab1ad0250c06d302c6ac4862bb4fdf425408c1ad08324102e8ac0b49920aa8c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-15T07:25:34Z","title_canon_sha256":"70351c627303cd9fb2b1b4b05fe80196daae7a8601a9eccc79359d13a6249902"},"schema_version":"1.0","source":{"id":"1706.04758","kind":"arxiv","version":2}},"canonical_sha256":"557a9eb3ce6825132767ef5deaf42312411237b332bafccfe786711f8d25786d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"557a9eb3ce6825132767ef5deaf42312411237b332bafccfe786711f8d25786d","first_computed_at":"2026-05-18T00:40:39.866395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:39.866395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jMwN6BY4eG59kxdoaAateZ7eMPxEAB1CBIndWLJaX//HCvBtW/tROoLKOatxIJBzxMyDG4L+0+kKwOwGiSqnAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:39.867101Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.04758","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b324d519143521bbf6ccf800e4793e77b4ec29142d61636ce9f1fb0f10fd85e","sha256:ef823f56d88121838b6d18a522dcdf1c7c7fcb4bd414bf08252f6490d8df92ee"],"state_sha256":"96d1082ef5f380c9b3207e8d77a45e2af728af73727b3a3c2d10ea59d0f84704"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A7+rOtGM8EWbhkMop5cPwFOLdJDbNlEsAvhOTTuIO7OjIvaRMdHZ8G+6x/HpvZjgardhaF5T4Mse6NCIv5cvAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T23:46:00.978760Z","bundle_sha256":"ccce71c63d365dab89a63d4c62e5210b4f88e3ddb440c3c3ca266b47caaf9448"}}