2D Gaussian Splatting for Bézier Spline Line Art Vectorization
Tianhao Chen, C. Fernandez, M. Oskarsson, C. Tappan, O. Sorkine-Hornung, M. Gross, C. Schroers, A. Djelouah
Proceedings of SIGGRAPH Conference Papers '26 (Los Angeles, USA, July 19-23, 2026), pp. 120:1-120:11
Abstract
Vectorization of line art and sketches is an important problem in computer graphics and remains an active area of research. In this paper, we leverage several recent advances in the field to introduce a new state-of-the-art method for line art vectorization. First, we show that it is possible to go beyond heuristics to extract a meaningful set of strokes from a raster image. We leverage both depth prediction and semantic feature extraction models to propose a more principled approach to split the skeleton graph of the sketch into subgraphs to initialize a set of strokes, producing results that better align with artistic intent. Second, we model strokes as BšŠzier curves with both geometric and appearance components, and employ 2D Gaussian splatting for fast, differentiable rendering. This formulation enables efficient fitting of strokes to the input while jointly optimizing control points and brush texture. We further demonstrate that the proposed framework naturally extends to video by incorporating temporal tracking and adaptive keyframe introduction, producing compact and temporally consistent stroke representations. Our evaluation shows that we achieve state-of-the-art results for image reconstruction, while being fast and producing strokes of high quality. More importantly, the fitting is highly compatible with user input, through the correction of the splines and the selection of optimization parameters.
