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tencent/HY-Motion-1.0

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HY-Motion 1.0: Scaling Flow Matching Models for 3D Motion Generation

🔥 News

—Dec 30, 2025: 🤗 We released the inference code and pretrained models of HY-Motion 1.0. Please give it a try via our HuggingFace Space and our Official Site!

Introduction

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.

Key Features

—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.

🔗 BibTeX

If you found this repository helpful, please cite our reports:

bibtex

@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}
}

Acknowledgements

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.