{"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/flag3d-a-3d-fitness-activity-dataset-with","title":"FLAG3D: A 3D Fitness Activity Dataset with Language Instruction","arxiv_id":"2212.04638","date":"2022-12-09","proceeding":"CVPR 2023 1","authors":["Yansong Tang","Jinpeng Liu","Aoyang Liu","Bin Yang","Wenxun Dai","Yongming Rao","Jiwen Lu","Jie zhou","Xiu Li"],"abstract":"With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github.io/FLAG3D.","url_abs":"https://arxiv.org/abs/2212.04638v2","url_pdf":"https://arxiv.org/pdf/2212.04638v2.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":"flag3d-a-3d-fitness-activity-dataset-with","repo_url":"https://github.com/2024-MindSpore-1/Code9/tree/main/LOGO--Mindspore-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-generation","task_name":"Action Generation"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"human-mesh-recovery","task_name":"Human Mesh Recovery"},{"task_slug":"human-action-generation","task_name":"Human action generation"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[{"slug":"flag3d","name":"FLAG3D","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.04638","atlas_url":"https://app.syntology.ai/?focus=2212.04638","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}