One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
Paper • 2511.22940 • Published • 2
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("MochunniaN1/One-to-All-14b", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")
prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)
output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
This repository contains the model and code for the paper One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer.
This project aims to provide a unified framework for high-fidelity character animation and image pose transfer for references with arbitrary layouts, addressing limitations in existing diffusion models regarding spatially misaligned reference-pose pairs.
- 📄 Paper
- 🌐 Project Page
- 💻 Code on GitHub
🎭 Showcase
Our model can adapt a single reference image to various motion patterns, demonstrating flexible motion control capabilities.
14B Model
| Reference | Motion 1 | Motion 2 | Motion 3 |
|---|---|---|---|
 |
 |
 |
 |
 |
 |
 |
 |
1.3B Model
The 1.3 B model also delivers strong performance (from 1.3b_2 ckpt).
| Reference | Motion 1 | Motion 2 | Motion 3 |
|---|---|---|---|
 |
 |
 |
 |
Also support longer video & out-of-domain cases
 |
 |
Acknowledgments
Our project is based on opensora. Some codes are brought from StableAnimator and Wan-Animate. Thanks for their awesome works.
📧 Contact
If you have any questions, please feel free to reach us at ssj180123@gmail.com
📝 Citation
If you find our work helpful or inspiring, please feel free to cite it.
@article{shi2025one,
title={One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer},
author={Shi, Shijun and Xu, Jing and Li, Zhihang and Peng, Chunli and Yang, Xiaoda and Lu, Lijing and Hu, Kai and Zhang, Jiangning},
journal={arXiv preprint arXiv:2511.22940},
year={2025}
}
- Downloads last month
- 344