HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation

February 09, 2026

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📝 Original Info

Title: HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation
ArXiv ID: 2512.23464
Date: 2025-12-29
Authors: Yuxin Wen, Qing Shuai, Di Kang, Jing Li, Cheng Wen, Yue Qian, Ningxin Jiao, Changhai Chen, Weijie Chen, Yiran Wang, Jinkun Guo, Dongyue An, Han Liu, Yanyu Tong, Chao Zhang, Qing Guo, Juan Chen, Qiao Zhang, Youyi Zhang, Zihao Yao, Cheng Zhang, Hong Duan, Xiaoping Wu, Qi Chen, Fei Cheng, Liang Dong, Peng He, Hao Zhang, Jiaxin Lin, Chao Zhang, Zhongyi Fan, Yifan Li, Zhichao Hu, Yuhong Liu, Linus, Jie Jiang, Xiaolong Li, Linchao Bao

📝 Abstract

We present HY-Motion 1.0, a series of state-of-the-art, large-scale, motion generation models capable of generating 3D human motions from textual descriptions. HY-Motion 1.0 represents the first successful attempt to scale up Diffusion Transformer (DiT)-based flow matching models to the billion-parameter scale within the motion generation domain, delivering instruction-following capabilities that significantly outperform current open-source benchmarks. Uniquely, we introduce a comprehensive, full-stage training paradigm -including large-scale pretraining on over 3,000 hours of motion data, high-quality fine-tuning on 400 hours of curated data, and reinforcement learning from both human feedback and reward models -to ensure precise alignment with the text instruction and high motion quality. This framework is supported by our meticulous data processing p...

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HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation

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