{"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/artiboost-boosting-articulated-3d-hand-object","title":"ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis","arxiv_id":"2109.05488","date":"2021-09-12","proceeding":"CVPR 2022 1","authors":["Kailin Li","Lixin Yang","Xinyu Zhan","Jun Lv","Wenqiang Xu","Jiefeng Li","Cewu Lu"],"abstract":"Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost.","url_abs":"https://arxiv.org/abs/2109.05488v2","url_pdf":"https://arxiv.org/pdf/2109.05488v2.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":"artiboost-boosting-articulated-3d-hand-object","repo_url":"https://github.com/lixiny/artiboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok"}},{"paper_slug":"artiboost-boosting-articulated-3d-hand-object","repo_url":"https://github.com/mvig-sjtu/artiboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-hand-pose-estimation","task_name":"3D Hand Pose Estimation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"hand-object-pose","task_name":"hand-object pose"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-ho-3d","task":"3D Hand Pose Estimation","dataset":"HO-3D v2","model":"ArtiBoost","rank_in_archive_order":20,"of":24,"metrics":{"AUC_J":"0.773","AUC_V":"0.782","F@15mm":"0.944","F@5mm":"0.488","PA-MPJPE (mm)":"11.4","PA-MPVPE":"10.9"},"uses_additional_data":false},{"leaderboard":"/sota/3d-hand-pose-estimation-on-ho-3d-v3","task":"3D Hand Pose Estimation","dataset":"HO-3D v3","model":"ArtiBoost","rank_in_archive_order":5,"of":8,"metrics":{"AUC_J":"0.785","AUC_V":"0.792","F@15mm":"0.946","F@5mm":"0.507","PA-MPJPE":"10.8","PA-MPVPE":"10.4"},"uses_additional_data":false},{"leaderboard":"/sota/hand-object-pose-on-dexycb","task":"hand-object pose","dataset":"DexYCB","model":"ArtiBoost","rank_in_archive_order":4,"of":9,"metrics":{"ADD-S":"-","Average MPJPE (mm)":"12.8","MCE":"-","OCE":"-","Procrustes-Aligned MPJPE":"-"},"uses_additional_data":false},{"leaderboard":"/sota/hand-object-pose-on-ho-3d","task":"hand-object pose","dataset":"HO-3D v2","model":"ArtiBoost","rank_in_archive_order":3,"of":9,"metrics":{"ADD-S":"-","Average MPJPE (mm)":"26.3","OME":"-","PA-MPJPE":"11.4","ST-MPJPE":"25.3"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.05488","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}