{"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/motionpcm-real-time-motion-synthesis-with","title":"MotionPCM: Real-Time Motion Synthesis with Phased Consistency Model","arxiv_id":"2501.19083","date":"2025-01-31","proceeding":null,"authors":["Lei Jiang","Ye Wei","Hao Ni"],"abstract":"Diffusion models have become a popular choice for human motion synthesis due to their powerful generative capabilities. However, their high computational complexity and large sampling steps pose challenges for real-time applications. Fortunately, the Consistency Model (CM) provides a solution to greatly reduce the number of sampling steps from hundreds to a few, typically fewer than four, significantly accelerating the synthesis of diffusion models. However, its application to text-conditioned human motion synthesis in latent space remains challenging. In this paper, we introduce \\textbf{MotionPCM}, a phased consistency model-based approach designed to improve the quality and efficiency of real-time motion synthesis in latent space.","url_abs":"https://arxiv.org/abs/2501.19083v1","url_pdf":"https://arxiv.org/pdf/2501.19083v1.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":[],"tasks":[{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-synthesis-on-humanml3d","task":"Motion Synthesis","dataset":"HumanML3D","model":"MotionPCM","rank_in_archive_order":2,"of":37,"metrics":{"Diversity":" 9.575","FID":"0.030","Multimodality":" 1.714","R Precision Top3":"0.842"},"uses_additional_data":false},{"leaderboard":"/sota/motion-synthesis-on-kit-motion-language","task":"Motion Synthesis","dataset":"KIT Motion-Language","model":"MotionPCM","rank_in_archive_order":11,"of":31,"metrics":{"Diversity":"10.827","FID":" 0.294","Multimodality":" 1.254","R Precision Top3":" 0.787"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.19083","atlas_url":"https://app.syntology.ai/?focus=2501.19083","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}