HY-Motion 1.0: Scaling Flow Matching Models for 3D Motion Generation
306
11 commits
9 linked in READMEs
updated May 22, 2026
HY-Motion 1.0 is a series of text-to-3D human motion generation models based on Diffusion Transformer (DiT) and Flow Matching. It allows developers to generate skeleton-based 3D character animations from simple text prompts, which can be directly integrated into various 3D animation pipelines. This model series is the first to scale DiT-based text-to-motion models to the billion-parameter level, achieving significant improvements in instruction-following capabilities and motion quality over existing open-source models.
State-of-the-Art Performance: Achieves state-of-the-art performance in both instruction-following capability and generated motion quality.
Billion-Scale Models: We are the first to successfully scale DiT-based models to the billion-parameter level for text-to-motion generation. This results in superior instruction understanding and following capabilities, outperforming comparable open-source models.
Advanced Three-Stage Training: Our models are trained using a comprehensive three-stage process:
Large-Scale Pre-training: Trained on over 3,000 hours of diverse motion data to learn a broad motion prior.
High-Quality Fine-tuning: Fine-tuned on 400 hours of curated, high-quality 3D motion data to enhance motion detail and smoothness.
Reinforcement Learning: Utilizes Reinforcement Learning from human feedback and reward models to further refine instruction-following and motion naturalness.
If you found this repository helpful, please cite our reports:
@article{hymotion2025,
title={HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation},
author={Tencent Hunyuan 3D Digital Human Team},
journal={arXiv preprint arXiv:2512.23464},
year={2025}
}
We would like to thank the contributors to the FLUX, diffusers, HuggingFace, SMPL/SMPLH, CLIP, Qwen3, PyTorch3D, kornia, transforms3d, FBX-SDK, GVHMR, and HunyuanVideo repositories or tools, for their open research and exploration.
HY-Motion 1.0: Scaling Flow Matching Models for 3D Motion Generation
306
11 commits
9 linked in READMEs
updated May 22, 2026
HY-Motion 1.0 is a series of text-to-3D human motion generation models based on Diffusion Transformer (DiT) and Flow Matching. It allows developers to generate skeleton-based 3D character animations from simple text prompts, which can be directly integrated into various 3D animation pipelines. This model series is the first to scale DiT-based text-to-motion models to the billion-parameter level, achieving significant improvements in instruction-following capabilities and motion quality over existing open-source models.
State-of-the-Art Performance: Achieves state-of-the-art performance in both instruction-following capability and generated motion quality.
Billion-Scale Models: We are the first to successfully scale DiT-based models to the billion-parameter level for text-to-motion generation. This results in superior instruction understanding and following capabilities, outperforming comparable open-source models.
Advanced Three-Stage Training: Our models are trained using a comprehensive three-stage process:
Large-Scale Pre-training: Trained on over 3,000 hours of diverse motion data to learn a broad motion prior.
High-Quality Fine-tuning: Fine-tuned on 400 hours of curated, high-quality 3D motion data to enhance motion detail and smoothness.
Reinforcement Learning: Utilizes Reinforcement Learning from human feedback and reward models to further refine instruction-following and motion naturalness.
If you found this repository helpful, please cite our reports:
@article{hymotion2025,
title={HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation},
author={Tencent Hunyuan 3D Digital Human Team},
journal={arXiv preprint arXiv:2512.23464},
year={2025}
}
We would like to thank the contributors to the FLUX, diffusers, HuggingFace, SMPL/SMPLH, CLIP, Qwen3, PyTorch3D, kornia, transforms3d, FBX-SDK, GVHMR, and HunyuanVideo repositories or tools, for their open research and exploration.