{"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/motion-x-a-large-scale-3d-expressive-whole","title":"Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset","arxiv_id":"2307.00818","date":"2023-07-03","proceeding":"NeurIPS 2023 11","authors":["Jing Lin","Ailing Zeng","Shunlin Lu","Yuanhao Cai","Ruimao Zhang","Haoqian Wang","Lei Zhang"],"abstract":"In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset. Existing motion datasets predominantly contain body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions. Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability. To overcome these limitations, we develop a whole-body motion and text annotation pipeline, which can automatically annotate motion from either single- or multi-view videos and provide comprehensive semantic labels for each video and fine-grained whole-body pose descriptions for each frame. This pipeline is of high precision, cost-effective, and scalable for further research. Based on it, we construct Motion-X, which comprises 15.6M precise 3D whole-body pose annotations (i.e., SMPL-X) covering 81.1K motion sequences from massive scenes. Besides, Motion-X provides 15.6M frame-level whole-body pose descriptions and 81.1K sequence-level semantic labels. Comprehensive experiments demonstrate the accuracy of the annotation pipeline and the significant benefit of Motion-X in enhancing expressive, diverse, and natural motion generation, as well as 3D whole-body human mesh recovery.","url_abs":"https://arxiv.org/abs/2307.00818v2","url_pdf":"https://arxiv.org/pdf/2307.00818v2.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":"motion-x-a-large-scale-3d-expressive-whole","repo_url":"https://github.com/idea-research/motion-x","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"human-mesh-recovery","task_name":"Human Mesh Recovery"},{"task_slug":"motion-generation","task_name":"Motion Generation"},{"task_slug":"text-annotation","task_name":"text annotation"}],"methods":[],"datasets_introduced":[{"slug":"motion-x","name":"Motion-X","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.00818","atlas_url":"https://app.syntology.ai/?focus=2307.00818","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}