Bézier Splatting for Fast and Differentiable Vector Graphics Rendering

Xi Liu, Chaoyi Zhou, Nanxuan Zhao, Siyu Huang

Keywords: differentiable vector graphics, Gaussian splatting

TL;DR: We propose Bézier Splatting, a new differentiable vector graphics representation that achieves an order-of-magnitude computational speedup compared to the state-of-the-art methods.

Abstract: Differentiable vector graphics (VGs) are widely used in image vectorization and vector synthesis, while existing representations are costly to optimize and struggle to achieve high-quality rendering results for high-resolution images. This work introduces a new differentiable VG representation, dubbed Bézier Splatting, that enables fast yet high-fidelity VG rasterization. Bézier Splatting samples 2D Gaussians along Bézier curves, which naturally provide positional gradients at object boundaries. Thanks to the efficient splatting-based differentiable rasterizer, Bézier Splatting achieves 30× and 150× faster per forward and backward rasterization step for open curves compared to DiffVG. Additionally, we introduce an adaptive pruning and densification strategy that dynamically adjusts the spatial distribution of curves to escape local minima, further improving VG quality. Furthermore, our new VG representation supports conversion to standard XML-based SVG format, enhancing interoperability with existing VG tools and pipelines. Experimental results show that Bézier Splatting significantly outperforms existing methods with better visual fidelity and significant optimization speedup.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 17469