2025/05/26 by Muyao Niu, Mingdeng Cao, Niu, Muyao +18 · 6 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Face recognition and analysis #Human Motion and Animation #cs.CV
paper · pdf · doi:10.48550/arxiv.2505.20255
Homepage: https://myniuuu.github.io/AniCrafter ; Codes: https://github.com/MyNiuuu/AniCrafter
arxiv created 2026/07/31 · arxiv updated 2026/08/03
Recent advances in video diffusion models have substantially enhanced character animation techniques. However, existing methods primarily depend on structural conditions, such as DWPose or SMPL-X, to animate character images, which limits their effectiveness in open-domain scenarios involving dynamic backgrounds or complex character-scene interactions. This study presents AniCrafter, a diffusion-based human-centric animation model designed to seamlessly integrate and animate a given character within open-domain dynamic backgrounds while adhering to specified human motion sequences. Built upon advanced Image-to-Video (I2V) diffusion architectures, the model introduces an innovative "avatar-background" conditioning mechanism that reformulates open-domain human-centric animation as a restoration problem, thereby achieving versatile, occlusion-aware animation results. Experimental evaluations demonstrate that the proposed approach outperforms current state-of-the-art methods and exhibits an exceptional capability in handling challenging scenarios. Codes and model are available at: https://github.com/MyNiuuu/AniCrafter