{"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/towards-robust-and-expressive-whole-body-1","title":"Towards Robust and Expressive Whole-body Human Pose and Shape Estimation","arxiv_id":"2312.08730","date":"2023-12-14","proceeding":"NeurIPS 2023 11","authors":["Hui EnPang","Zhongang Cai","Lei Yang","Qingyi Tao","Zhonghua Wu","Tianwei Zhang","Ziwei Liu"],"abstract":"Whole-body pose and shape estimation aims to jointly predict different behaviors (e.g., pose, hand gesture, facial expression) of the entire human body from a monocular image. Existing methods often exhibit degraded performance under the complexity of in-the-wild scenarios. We argue that the accuracy and reliability of these models are significantly affected by the quality of the predicted \\textit{bounding box}, e.g., the scale and alignment of body parts. The natural discrepancy between the ideal bounding box annotations and model detection results is particularly detrimental to the performance of whole-body pose and shape estimation. In this paper, we propose a novel framework to enhance the robustness of whole-body pose and shape estimation. Our framework incorporates three new modules to address the above challenges from three perspectives: \\textbf{1) Localization Module} enhances the model's awareness of the subject's location and semantics within the image space. \\textbf{2) Contrastive Feature Extraction Module} encourages the model to be invariant to robust augmentations by incorporating contrastive loss with dedicated positive samples. \\textbf{3) Pixel Alignment Module} ensures the reprojected mesh from the predicted camera and body model parameters are accurate and pixel-aligned. We perform comprehensive experiments to demonstrate the effectiveness of our proposed framework on body, hands, face and whole-body benchmarks. Codebase is available at \\url{https://github.com/robosmplx/robosmplx}.","url_abs":"https://arxiv.org/abs/2312.08730v1","url_pdf":"https://arxiv.org/pdf/2312.08730v1.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":"towards-robust-and-expressive-whole-body-1","repo_url":"https://github.com/robosmplx/robosmplx","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2312.08730","atlas_url":"https://app.syntology.ai/?focus=2312.08730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08730"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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