{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semantic-graph-convolutional-networks-for-3d","title":"Semantic Graph Convolutional Networks for 3D Human Pose Regression","arxiv_id":"1904.03345","date":"2019-04-06","proceeding":"CVPR 2019 6","authors":["Long Zhao","Xi Peng","Yu Tian","Mubbasir Kapadia","Dimitris N. Metaxas"],"abstract":"In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.","url_abs":"https://arxiv.org/abs/1904.03345v3","url_pdf":"https://arxiv.org/pdf/1904.03345v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantic-graph-convolutional-networks-for-3d","repo_url":"https://github.com/garyzhao/SemGCN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"semantic-graph-convolutional-networks-for-3d","repo_url":"https://github.com/happyvictor008/High-order-GNN-LF-iter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"semantic-graph-convolutional-networks-for-3d","repo_url":"https://github.com/sjtuxcx/ITES","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"semantic-graph-convolutional-networks-for-3d","repo_url":"https://github.com/tamasino52/Any-GCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"semantic-graph-convolutional-networks-for-3d","repo_url":"https://github.com/zhimingzo/modulated-gcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"monocular-3d-human-pose-estimation","task_name":"Monocular 3D Human Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"graph-convolutional-networks","method_name":"Graph Convolutional Networks"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"SemGCN","rank_in_archive_order":79,"of":88,"metrics":{"Average MPJPE (mm)":"57.6","Multi-View or Monocular":"Monocular","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/monocular-3d-human-pose-estimation-on-human3","task":"Monocular 3D Human Pose Estimation","dataset":"Human3.6M","model":"SemGCN","rank_in_archive_order":28,"of":52,"metrics":{"Average MPJPE (mm)":"57.6","Frames Needed":"1","Need Ground Truth 2D Pose":"No","Use Video Sequence":"No"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03345"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tamasino52/Any-GCN","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/garyzhao/SemGCN","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhimingzo/modulated-gcn","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sjtuxcx/ITES","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/happyvictor008/High-order-GNN-LF-iter","reach":{"status":"unanswered"}}],"summary":{"ran":2,"unverified":6},"by_repo_kind":{"official":{"samples":8,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3afb541a8147d123","entry":"mpjpe","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/loss.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/loss.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3afb541a8147d123"}},{"code_sha256_prefix":"29d56deadc8fcd3c","entry":"p_mpjpe","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/loss.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/loss.py","link_basis":"plan_row","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"29d56deadc8fcd3c"}},{"code_sha256_prefix":"c1285546d25a021d","entry":"create_2d_data","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/data_utils.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c1285546d25a021d"}},{"code_sha256_prefix":"a01e37bb132ecaf4","entry":"fetch","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/data_utils.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a01e37bb132ecaf4"}},{"code_sha256_prefix":"7669d6b1ce1e7096","entry":"image_coordinates","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/camera.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/camera.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7669d6b1ce1e7096"}},{"code_sha256_prefix":"82baf6aa4fb040a1","entry":"normalize_screen_coordinates","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/camera.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/camera.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"82baf6aa4fb040a1"}},{"code_sha256_prefix":"2bc24c0c1c934d2d","entry":"read_3d_data","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/data_utils.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2bc24c0c1c934d2d"}},{"code_sha256_prefix":"6dd3ba892d9349d8","entry":"weighted_mpjpe","repo":"garyzhao/SemGCN","repo_kind":"official","path":"common/loss.py","file_url":"https://github.com/garyzhao/SemGCN/blob/HEAD/common/loss.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6dd3ba892d9349d8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}