{"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/3dcrowdnet-2d-human-pose-guided3d-crowd-human","title":"Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes","arxiv_id":"2104.07300","date":"2021-04-15","proceeding":"CVPR 2022 1","authors":["Hongsuk Choi","Gyeongsik Moon","JoonKyu Park","Kyoung Mu Lee"],"abstract":"We consider the problem of recovering a single person's 3D human mesh from in-the-wild crowded scenes. While much progress has been in 3D human mesh estimation, existing methods struggle when test input has crowded scenes. The first reason for the failure is a domain gap between training and testing data. A motion capture dataset, which provides accurate 3D labels for training, lacks crowd data and impedes a network from learning crowded scene-robust image features of a target person. The second reason is a feature processing that spatially averages the feature map of a localized bounding box containing multiple people. Averaging the whole feature map makes a target person's feature indistinguishable from others. We present 3DCrowdNet that firstly explicitly targets in-the-wild crowded scenes and estimates a robust 3D human mesh by addressing the above issues. First, we leverage 2D human pose estimation that does not require a motion capture dataset with 3D labels for training and does not suffer from the domain gap. Second, we propose a joint-based regressor that distinguishes a target person's feature from others. Our joint-based regressor preserves the spatial activation of a target by sampling features from the target's joint locations and regresses human model parameters. As a result, 3DCrowdNet learns target-focused features and effectively excludes the irrelevant features of nearby persons. We conduct experiments on various benchmarks and prove the robustness of 3DCrowdNet to the in-the-wild crowded scenes both quantitatively and qualitatively. The code is available at https://github.com/hongsukchoi/3DCrowdNet_RELEASE.","url_abs":"https://arxiv.org/abs/2104.07300v3","url_pdf":"https://arxiv.org/pdf/2104.07300v3.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":"3dcrowdnet-2d-human-pose-guided3d-crowd-human","repo_url":"https://github.com/hongsukchoi/3dcrowdnet_release","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"2d-human-pose-estimation","task_name":"2D Human Pose Estimation"},{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"3d-multi-person-human-pose-estimation","task_name":"3D Multi-Person Human Pose Estimation"},{"task_slug":"3d-multi-person-pose-estimation","task_name":"3D Multi-Person Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-3dpw","task":"3D Human Pose Estimation","dataset":"3DPW","model":"3DCrowdNet","rank_in_archive_order":58,"of":119,"metrics":{"MPJPE":"85.8","MPVPE":"108.5","PA-MPJPE":"55.8"},"uses_additional_data":false},{"leaderboard":"/sota/3d-multi-person-human-pose-estimation-on","task":"3D Multi-Person Pose Estimation","dataset":"MuPoTS-3D","model":"3DCrowdNet (HigherHRNet)","rank_in_archive_order":10,"of":10,"metrics":{"3DPCK":"72.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2104.07300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07300"}},"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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