PhysAnimator: Physics-Guided Generative Cartoon Animation

1Netflix, 2University of California, Los Angeles

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.

Method

Our approach begin by segmenting the object and creating a triangulated deformable mesh. Physics-based simulation are then used to generate dynamic optical flow fields, with users given the option to guide the motion through customizable energy strokes (shown as orange arrows) and rigging points (shown as red dots). The extracted sketch is warped using the computed optical flow and refined with a sketch-guided video diffusion model, producing a smooth, stylized animation sequence. Optionally, a cartoon interpolation model can further be applied to enhance the animation with expressive dynamics.

Comparisons with Video Models

Input

Input Image

DragAnything

Motion-I2V

DynamiCrafter

Cinemo

Ours

Input

Input Image

DragAnything

Motion-I2V

DynamiCrafter

Cinemo

Ours

Input

Input Image

DragAnything

Motion-I2V

DynamiCrafter

Cinemo

Ours

Input

Input Image

DragAnything

Motion-I2V

DynamiCrafter

Cinemo

Ours

Input

Input Image

DragAnything

Motion-I2V

DynamiCrafter

Cinemo

Ours

Controllable Generation

Controllable Generation
Controllable Generation
Controllable Generation
Controllable Generation

BibTeX

@article{xie2025physanimator,
        title={PhysAnimator: Physics-Guided Generative Cartoon Animation},
        author={Xie, Tianyi and Zhao, Yiwei and Jiang, Ying and Jiang, Chenfanfu},
        journal={arXiv preprint arXiv:2501.16550},
        year={2025}
      }