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arxiv: 2505.20255 · v2 · pith:AGTAMJ3W · submitted 2025-05-26 · cs.CV

AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

Muyao Niu , Mingdeng Cao , Yifan Zhan , Qingtian Zhu , Mingze Ma , Jiancheng Zhao , Yanhong Zeng , Zhihang Zhong
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Xiao Sun Yinqiang Zheng
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classification cs.CV
keywords animationanicraftercharacterdiffusionhuman-centricopen-domainanimateavatar-background
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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.

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