{"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/3d-hand-shape-and-pose-from-images-in-the","title":"3D Hand Shape and Pose from Images in the Wild","arxiv_id":"1902.03451","date":"2019-02-09","proceeding":"CVPR 2019 6","authors":["Adnane Boukhayma","Rodrigo de Bem","Philip H. S. Torr"],"abstract":"We present in this work the first end-to-end deep learning based method that\npredicts both 3D hand shape and pose from RGB images in the wild. Our network\nconsists of the concatenation of a deep convolutional encoder, and a fixed\nmodel-based decoder. Given an input image, and optionally 2D joint detections\nobtained from an independent CNN, the encoder predicts a set of hand and view\nparameters. The decoder has two components: A pre-computed articulated mesh\ndeformation hand model that generates a 3D mesh from the hand parameters, and a\nre-projection module controlled by the view parameters that projects the\ngenerated hand into the image domain. We show that using the shape and pose\nprior knowledge encoded in the hand model within a deep learning framework\nyields state-of-the-art performance in 3D pose prediction from images on\nstandard benchmarks, and produces geometrically valid and plausible 3D\nreconstructions. Additionally, we show that training with weak supervision in\nthe form of 2D joint annotations on datasets of images in the wild, in\nconjunction with full supervision in the form of 3D joint annotations on\nlimited available datasets allows for good generalization to 3D shape and pose\npredictions on images in the wild.","url_abs":"http://arxiv.org/abs/1902.03451v1","url_pdf":"http://arxiv.org/pdf/1902.03451v1.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":"3d-hand-shape-and-pose-from-images-in-the","repo_url":"https://github.com/boukhayma/3dhand","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"3d-hand-shape-and-pose-from-images-in-the","repo_url":"https://github.com/yihui-he/epipolar-transformers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-hand-pose-estimation","task_name":"3D Hand Pose Estimation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"pose-prediction","task_name":"Pose Prediction"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-freihand","task":"3D Hand Pose Estimation","dataset":"FreiHAND","model":"Boukhayma et al.","rank_in_archive_order":32,"of":33,"metrics":{"PA-F@15mm":"0.898","PA-F@5mm":"0.435","PA-MPVPE":"13.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.03451","atlas_url":"https://app.syntology.ai/?focus=1902.03451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.03451"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/yihui-he/epipolar-transformers","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/boukhayma/3dhand","reach":null}],"summary":{"ran_fixture":2},"by_repo_kind":{"listed":{"samples":2,"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":2,"samples":[{"code_sha256_prefix":"66f62ca7ae552215","entry":"get_poseweights","repo":"boukhayma/3dhand","repo_kind":"listed","path":"model.py","file_url":"https://github.com/boukhayma/3dhand/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"66f62ca7ae552215"}},{"code_sha256_prefix":"e3c1e8bdb0cc90e2","entry":"rodrigues","repo":"boukhayma/3dhand","repo_kind":"listed","path":"model.py","file_url":"https://github.com/boukhayma/3dhand/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e3c1e8bdb0cc90e2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}