{"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-human-pose-estimation-in-video-with","title":"3D human pose estimation in video with temporal convolutions and semi-supervised training","arxiv_id":"1811.11742","date":"2018-11-28","proceeding":"CVPR 2019 6","authors":["Dario Pavllo","Christoph Feichtenhofer","David Grangier","Michael Auli"],"abstract":"In this work, we demonstrate that 3D poses in video can be effectively\nestimated with a fully convolutional model based on dilated temporal\nconvolutions over 2D keypoints. We also introduce back-projection, a simple and\neffective semi-supervised training method that leverages unlabeled video data.\nWe start with predicted 2D keypoints for unlabeled video, then estimate 3D\nposes and finally back-project to the input 2D keypoints. In the supervised\nsetting, our fully-convolutional model outperforms the previous best result\nfrom the literature by 6 mm mean per-joint position error on Human3.6M,\ncorresponding to an error reduction of 11%, and the model also shows\nsignificant improvements on HumanEva-I. Moreover, experiments with\nback-projection show that it comfortably outperforms previous state-of-the-art\nresults in semi-supervised settings where labeled data is scarce. Code and\nmodels are available at https://github.com/facebookresearch/VideoPose3D","url_abs":"http://arxiv.org/abs/1811.11742v2","url_pdf":"http://arxiv.org/pdf/1811.11742v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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