Recent advances in video diffusion models have significantly improved character animation techniques. However, current approaches rely on basic structural conditions such as DWPose or SMPL-X to animate character images, limiting their effectiveness in open-domain scenarios with dynamic backgrounds or challenging human poses. In this paper, we introduce \textbf{AniCrafter}, a diffusion-based human-centric animation model that can seamlessly integrate and animate a given character into open-domain dynamic backgrounds while following given human motion sequences. Built on cutting-edge Image-to-Video (I2V) diffusion architectures, our model incorporates an innovative ''avatar-background'' conditioning mechanism that reframes open-domain human-centric animation as a restoration task, enabling more stable and versatile animation outputs. Experimental results demonstrate the superior performance of our method. Codes are available at https://github.com/MyNiuuu/AniCrafter.
@article{arxiv.2505.20255,
title = {AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models},
author = {Muyao Niu and Mingdeng Cao and Yifan Zhan and Qingtian Zhu and Mingze Ma and Jiancheng Zhao and Yanhong Zeng and Zhihang Zhong and Xiao Sun and Yinqiang Zheng},
journal= {arXiv preprint arXiv:2505.20255},
year = {2025}
}