{"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/solopose-one-shot-kinematic-3d-human-pose","title":"SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation","arxiv_id":"2312.10195","date":"2023-12-15","proceeding":null,"authors":["David C. Jeong","Hongji Liu","Saunder Salazar","Jessie Jiang","Christopher A. Kitts"],"abstract":"While recent two-stage many-to-one deep learning models have demonstrated great success in 3D human pose estimation, such models are inefficient ways to detect 3D key points in a sequential video relative to one-shot and many-to-many models. Another key drawback of two-stage and many-to-one models is that errors in the first stage will be passed onto the second stage. In this paper, we introduce SoloPose, a novel one-shot, many-to-many spatio-temporal transformer model for kinematic 3D human pose estimation of video. SoloPose is further fortified by HeatPose, a 3D heatmap based on Gaussian Mixture Model distributions that factors target key points as well as kinematically adjacent key points. Finally, we address data diversity constraints with the 3D AugMotion Toolkit, a methodology to augment existing 3D human pose datasets, specifically by projecting four top public 3D human pose datasets (Humans3.6M, MADS, AIST Dance++, MPI INF 3DHP) into a novel dataset (Humans7.1M) with a universal coordinate system. Extensive experiments are conducted on Human3.6M as well as the augmented Humans7.1M dataset, and SoloPose demonstrates superior results relative to the state-of-the-art approaches.","url_abs":"https://arxiv.org/abs/2312.10195v1","url_pdf":"https://arxiv.org/pdf/2312.10195v1.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":"solopose-one-shot-kinematic-3d-human-pose","repo_url":"https://github.com/Santa-Clara-Media-Lab/SoloPose","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[{"method_slug":"heatmap","method_name":"Heatmap"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"SoloPose","rank_in_archive_order":15,"of":88,"metrics":{"Average MPJPE (mm)":" 38.9","Multi-View or Monocular":"Monocular","PA-MPJPE":"29.9","Using 2D ground-truth joints":"No"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}