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PhysAnimator: Physics-Guided Generative Cartoon Animation

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arxiv 2501.16550 v2 pith:D5YXX2DZ submitted 2025-01-27 cs.GR cs.CV

classification cs.GRcs.CV
keywords animationanimationsphysanimatoranimecreatingdynamicgeneratinggenerative
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    VideoREPA adds a token-relation distillation loss that aligns a text-to-video diffusion model's internal features with VideoMAEv2, boosting physical commonsense scores on VideoPhy and VideoPhy2.

  2. RoboScape: Physics-informed Embodied World Model

    cs.CV 2025-06 conditional novelty 5.0 of 10

    RoboScape jointly learns RGB video, depth, and keypoint-token consistency in one autoregressive world model, improving video quality, geometry, action control, synthetic-data policy training, and policy evaluation for...

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