English

PhysAnimator: Physics-Guided Generative Cartoon Animation

Graphics 2025-03-27 v2 Computer Vision and Pattern Recognition

Abstract

Creating hand-drawn animation sequences is labor-intensive and demands professional expertise. We introduce PhysAnimator, a novel approach for generating physically plausible meanwhile anime-stylized animation from static anime illustrations. Our method seamlessly integrates physics-based simulations with data-driven generative models to produce dynamic and visually compelling animations. To capture the fluidity and exaggeration characteristic of anime, we perform image-space deformable body simulations on extracted mesh geometries. We enhance artistic control by introducing customizable energy strokes and incorporating rigging point support, enabling the creation of tailored animation effects such as wind interactions. Finally, we extract and warp sketches from the simulation sequence, generating a texture-agnostic representation, and employ a sketch-guided video diffusion model to synthesize high-quality animation frames. The resulting animations exhibit temporal consistency and visual plausibility, demonstrating the effectiveness of our method in creating dynamic anime-style animations. See our project page for more demos: https://xpandora.github.io/PhysAnimator/

Keywords

Cite

@article{arxiv.2501.16550,
  title  = {PhysAnimator: Physics-Guided Generative Cartoon Animation},
  author = {Tianyi Xie and Yiwei Zhao and Ying Jiang and Chenfanfu Jiang},
  journal= {arXiv preprint arXiv:2501.16550},
  year   = {2025}
}

Comments

Accepted by CVPR 2025

R2 v1 2026-06-28T21:20:55.156Z