{"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/exploiting-temporal-information-for-3d-pose","title":"Exploiting temporal information for 3D pose estimation","arxiv_id":"1711.08585","date":"2017-11-23","proceeding":null,"authors":["Mir Rayat Imtiaz Hossain","James J. Little"],"abstract":"In this work, we address the problem of 3D human pose estimation from a\nsequence of 2D human poses. Although the recent success of deep networks has\nled many state-of-the-art methods for 3D pose estimation to train deep networks\nend-to-end to predict from images directly, the top-performing approaches have\nshown the effectiveness of dividing the task of 3D pose estimation into two\nsteps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from\nimages and then mapping them into 3D space. They also showed that a\nlow-dimensional representation like 2D locations of a set of joints can be\ndiscriminative enough to estimate 3D pose with high accuracy. However,\nestimation of 3D pose for individual frames leads to temporally incoherent\nestimates due to independent error in each frame causing jitter. Therefore, in\nthis work we utilize the temporal information across a sequence of 2D joint\nlocations to estimate a sequence of 3D poses. We designed a\nsequence-to-sequence network composed of layer-normalized LSTM units with\nshortcut connections connecting the input to the output on the decoder side and\nimposed temporal smoothness constraint during training. We found that the\nknowledge of temporal consistency improves the best reported result on\nHuman3.6M dataset by approximately $12.2\\%$ and helps our network to recover\ntemporally consistent 3D poses over a sequence of images even when the 2D pose\ndetector fails.","url_abs":"http://arxiv.org/abs/1711.08585v4","url_pdf":"http://arxiv.org/pdf/1711.08585v4.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":"exploiting-temporal-information-for-3d-pose","repo_url":"https://github.com/rayat137/Pose_3D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"Sequence-to-sequence network","rank_in_archive_order":81,"of":88,"metrics":{"Average MPJPE (mm)":"58.5","Multi-View or Monocular":"Monocular","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-humaneva-i","task":"3D Human Pose Estimation","dataset":"HumanEva-I","model":"Sequence-to-sequence network","rank_in_archive_order":14,"of":31,"metrics":{"Mean Reconstruction Error (mm)":"22"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.08585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08585"}},"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. 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