{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:I3SFEOD3GBHCG227JL5QL525NG","short_pith_number":"pith:I3SFEOD3","schema_version":"1.0","canonical_sha256":"46e452387b304e236b5f4afb05f75d69a25063df6e50f50c2855d7f453b7da37","source":{"kind":"arxiv","id":"1812.11328","version":1},"attestation_state":"computed","paper":{"title":"Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akihiko Murai, Ko Ayusawa, Ryusuke Sagawa, Yusuke Yoshiyasu","submitted_at":"2018-12-29T10:22:14Z","abstract_excerpt":"In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image. This in turn allows us to generate skinned mesh animations. Here, we propose a two-step regression approach. The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure. The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and z"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1812.11328","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T10:22:14Z","cross_cats_sorted":[],"title_canon_sha256":"d3d0b73f4c93a6eeaaa564643b0568005e3df12df9437fcb63a64b7aa201ac18","abstract_canon_sha256":"f623ab3f3162fc0678d3e07c25160017fce45264b5a08a80bc0ec862484dd540"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:12.228552Z","signature_b64":"nEEQKfZwTIcafcZEFn+gATmdz9ff+9B1hY3dFiv6yQE4l75xGR1g5iMbJGHcnx1k/1Tuw0oSnw0ELTBixuhuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46e452387b304e236b5f4afb05f75d69a25063df6e50f50c2855d7f453b7da37","last_reissued_at":"2026-05-17T23:57:12.228157Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:12.228157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akihiko Murai, Ko Ayusawa, Ryusuke Sagawa, Yusuke Yoshiyasu","submitted_at":"2018-12-29T10:22:14Z","abstract_excerpt":"In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image. This in turn allows us to generate skinned mesh animations. Here, we propose a two-step regression approach. The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure. The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and z"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11328","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1812.11328","created_at":"2026-05-17T23:57:12.228213+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.11328v1","created_at":"2026-05-17T23:57:12.228213+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11328","created_at":"2026-05-17T23:57:12.228213+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3SFEOD3GBHC","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3SFEOD3GBHCG227","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3SFEOD3","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG","json":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG.json","graph_json":"https://pith.science/api/pith-number/I3SFEOD3GBHCG227JL5QL525NG/graph.json","events_json":"https://pith.science/api/pith-number/I3SFEOD3GBHCG227JL5QL525NG/events.json","paper":"https://pith.science/paper/I3SFEOD3"},"agent_actions":{"view_html":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG","download_json":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG.json","view_paper":"https://pith.science/paper/I3SFEOD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.11328&json=true","fetch_graph":"https://pith.science/api/pith-number/I3SFEOD3GBHCG227JL5QL525NG/graph.json","fetch_events":"https://pith.science/api/pith-number/I3SFEOD3GBHCG227JL5QL525NG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG/action/storage_attestation","attest_author":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG/action/author_attestation","sign_citation":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG/action/citation_signature","submit_replication":"https://pith.science/pith/I3SFEOD3GBHCG227JL5QL525NG/action/replication_record"}},"created_at":"2026-05-17T23:57:12.228213+00:00","updated_at":"2026-05-17T23:57:12.228213+00:00"}