VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation

2026-05-02

AI Summary

Existing methods for text-to-SVG animation generation struggle to simultaneously preserve topological consistency and model non-rigid deformations, while also lacking support for open-domain instructions. This work proposes VAnim—the first large language model framework tailored for open-domain, text-driven SVG animation generation—which formulates animation as sparse state updates over a persistent SVG DOM tree, drastically reducing sequence length by more than 9.8×. By integrating an identity-aware motion planning mechanism and rendering-aware reinforcement learning (GRPO) with a video-perceptual hybrid reward, VAnim achieves high-fidelity dynamic generation while maintaining structural validity and identity consistency. We further introduce SVGAnim-134k, the first vector animation benchmark, and demonstrate through experiments that VAnim significantly outperforms existing approaches in semantic alignment, motion quality, and structural preservation.

Abstract

Scalable Vector Graphics (SVG) animation generation is pivotal for professional design due to their structural editability and resolution independence. However, this task remains challenging as it requires bridging discrete code representations with continuous visual dynamics. Existing optimization-based methods often destroy topological consistency, while general-purpose LLMs rely on rigid CSS/SMIL transformations, failing to model geometry-level non-rigid deformations. To address these limitations, we present VAnim, the first LLM-based framework for open-domain text-to-SVG animation. We reconceptualize animation not as sequence generation, but as Sparse State Updates (SSU) on a persistent SVG DOM tree. This paradigm compresses sequence length by over 9.8x while preserving the SVG DOM structure and non-participating elements by construction. To enable precise control, we propose an Identification-First Motion Planning mechanism that grounds textual instructions in explicit visual entities. Furthermore, to overcome the non-differentiable nature of SVG rendering, we employ Rendering-Aware Reinforcement Learning via Group Relative Policy Optimization (GRPO). By leveraging a hybrid reward from a state-of-the-art video perception encoder, we align discrete code updates with high-fidelity visual feedback. We also introduce SVGAnim-134k, the first benchmark for vector animation. Extensive experiments demonstrate that VAnim significantly outperforms state-of-the-art baselines in semantic alignment and structural validity, with additional appendix metrics further validating motion quality and identity preservation.

Problem

• SVG animation
• structural preservation
• non-rigid deformation
• text-to-animation
• vector graphics

Innovation

• Sparse State Updates
• Rendering-Aware Reinforcement Learning
• SVG Animation
• DOM-Preserving Modeling
• Group Relative Policy Optimization