{"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/renderih-a-large-scale-synthetic-dataset-for","title":"RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation","arxiv_id":"2309.09301","date":"2023-09-17","proceeding":"ICCV 2023 1","authors":["Lijun Li","Linrui Tian","Xindi Zhang","Qi Wang","Bang Zhang","Mengyuan Liu","Chen Chen"],"abstract":"The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://github.com/adwardlee/RenderIH.","url_abs":"https://arxiv.org/abs/2309.09301v3","url_pdf":"https://arxiv.org/pdf/2309.09301v3.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":"renderih-a-large-scale-synthetic-dataset-for","repo_url":"https://github.com/adwardlee/renderih","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"3d-interacting-hand-pose-estimation","task_name":"3D Interacting Hand Pose Estimation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"hand-pose-estimation","task_name":"Hand Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2309.09301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.09301"}},"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":"deterministic:regex_extraction","url":"https://github.com/adwardlee/RenderIH","reach":{"status":"ok","spdx":"GPL-3.0"}}],"summary":{"ran":6,"unverified":2},"by_repo_kind":{"official":{"samples":8,"ran":6,"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":8,"samples":[{"code_sha256_prefix":"562787578bf4c568","entry":"batch_rodrigues","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/Loss_mano.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/Loss_mano.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"562787578bf4c568"}},{"code_sha256_prefix":"7a1709f1754edbf0","entry":"flip_pose","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/loader_mano.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/loader_mano.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"7a1709f1754edbf0"}},{"code_sha256_prefix":"5ca9f7dcd155aa67","entry":"make_linear_layers","repo":"adwardlee/RenderIH","repo_kind":"official","path":"common/net_inverse.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/common/net_inverse.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"5ca9f7dcd155aa67"}},{"code_sha256_prefix":"ae4ca2a089067b0a","entry":"quat2mat","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/Loss_mano.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/Loss_mano.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"ae4ca2a089067b0a"}},{"code_sha256_prefix":"3830b5b96e0ecb42","entry":"rodrigues_batch","repo":"adwardlee/RenderIH","repo_kind":"official","path":"common/net_inverse.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/common/net_inverse.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"3830b5b96e0ecb42"}},{"code_sha256_prefix":"014f1419efcfbd16","entry":"rot_aa","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/loader_mano.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/loader_mano.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"014f1419efcfbd16"}},{"code_sha256_prefix":"01039b93fb695623","entry":"calc_aux_loss","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/Loss.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/Loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"01039b93fb695623"}},{"code_sha256_prefix":"9648927ec0d6c642","entry":"calc_loss_GCN","repo":"adwardlee/RenderIH","repo_kind":"official","path":"core/Loss.py","file_url":"https://github.com/adwardlee/RenderIH/blob/HEAD/core/Loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"9648927ec0d6c642"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}