{"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/an-integral-pose-regression-system-for-the","title":"An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge","arxiv_id":"1809.06079","date":"2018-09-17","proceeding":null,"authors":["Xiao Sun","Chuankang Li","Stephen Lin"],"abstract":"For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation\nsystem based mainly on the integral human pose regression method. We show a\ncomprehensive ablation study to examine the key performance factors of the\nproposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset,\nplacing second in the ECCV2018 3D human pose estimation challenge. Code will be\nreleased to facilitate future work.","url_abs":"http://arxiv.org/abs/1809.06079v1","url_pdf":"http://arxiv.org/pdf/1809.06079v1.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":"an-integral-pose-regression-system-for-the","repo_url":"https://github.com/JimmySuen/integral-human-pose","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-chall-h80k","task":"3D Human Pose Estimation","dataset":"CHALL H80K","model":"ResNet","rank_in_archive_order":1,"of":1,"metrics":{"MPJPE":"55.3"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.06079","atlas_url":"https://app.syntology.ai/?focus=1809.06079","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}