--- license: cc-by-nc-sa-4.0 library_name: diffusers pipeline_tag: image-to-video tags: - human-animation - pose-guided - DiT --- # HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex Motions arxiv  Github  License  This repository contains the model weights for **HyperMotion**, presented in the paper [HyperMotionX: The Dataset and Benchmark with DiT-Based Pose-Guided Human Image Animation of Complex Motions](https://huggingface.co/papers/2505.22977). ## Introduction Recent advances in diffusion models have significantly improved conditional video generation, particularly in the pose-guided human image animation task. Although existing methods are capable of generating high-fidelity and time-consistent animation sequences in regular motions and static scenes, there are still obvious limitations when facing complex human body motions (Hypermotion) that contain highly dynamic, non-standard motions. To address this challenge, we introduce the **Open-HyperMotionX Dataset** and **HyperMotionX Bench**, which provide high-quality human pose annotations and curated video clips for evaluating and improving pose-guided human image animation models under complex human motion conditions. Furthermore, we propose a simple yet powerful DiT-based video generation baseline adopting [Wan2.1-I2V-14B](https://github.com/Wan-Video/Wan2.1) as the base model and design spatial low-frequency enhanced RoPE. ## Inference To use the model, you can refer to the inference scripts provided in the official [GitHub repository](https://github.com/vivoCameraResearch/Hyper-Motion). ```python import torch # Config and model path config_path = "config/wan2.1/wan_civitai.yaml" model_name = "shuolin/HyperMotion" # model checkpoints # Use torch.float16 if GPU does not support torch.bfloat16 weight_dtype = torch.bfloat16 control_video = "path/to/pose_video.mp4" # guided pose video ref_image = "path/to/image.jpg" # reference image # For detailed implementation, please refer to scripts/inference.py in the official repo. ``` ## Citation ```bibtex @article{xu2025hypermotion, title={Hypermotion: Dit-based pose-guided human image animation of complex motions}, author={Xu, Shuolin and Zheng, Siming and Wang, Ziyi and Yu, HC and Chen, Jinwei and Zhang, Huaqi, and Zhou Daquan, and Tong-Yee Lee, and Li, Bo and Jiang, Peng-Tao}, journal={arXiv preprint arXiv:2505.22977}, year={2025} } ```