{"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/estimating-egocentric-3d-human-pose-in-the","title":"Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision","arxiv_id":"2201.07929","date":"2022-01-20","proceeding":"CVPR 2022 1","authors":["Jian Wang","Lingjie Liu","Weipeng Xu","Kripasindhu Sarkar","Diogo Luvizon","Christian Theobalt"],"abstract":"Egocentric 3D human pose estimation with a single fisheye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from in-the-wild images, because they can only be trained on synthetic data due to the unavailability of large-scale in-the-wild egocentric datasets. Furthermore, these methods easily fail when the body parts are occluded by or interacting with the surrounding scene. To address the shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured by a head-mounted fisheye camera and an auxiliary external camera, which provides an additional observation of the human body from a third-person perspective during training. We present a new egocentric pose estimation method, which can be trained on the new dataset with weak external supervision. Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision. The pseudo labels are then used to train an egocentric pose estimation network. To facilitate the network training, we propose a novel learning strategy to supervise the egocentric features with the high-quality features extracted by a pretrained external-view pose estimation model. The experiments show that our method predicts accurate 3D poses from a single in-the-wild egocentric image and outperforms the state-of-the-art methods both quantitatively and qualitatively.","url_abs":"https://arxiv.org/abs/2201.07929v1","url_pdf":"https://arxiv.org/pdf/2201.07929v1.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":[],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"egocentric-pose-estimation","task_name":"Egocentric Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/egocentric-pose-estimation-on-globalegomocap","task":"Egocentric Pose Estimation","dataset":"GlobalEgoMocap Test Dataset","model":"EgoPW","rank_in_archive_order":4,"of":7,"metrics":{"Average MPJPE (mm)":"81.71","PA-MPJPE":"64.87"},"uses_additional_data":true},{"leaderboard":"/sota/egocentric-pose-estimation-on-sceneego","task":"Egocentric Pose Estimation","dataset":"SceneEgo","model":"EgoPW","rank_in_archive_order":6,"of":8,"metrics":{"Average MPJPE (mm)":"189.6","PA-MPJPE":"105.3"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.07929","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}