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 Bézier curves with both geometric and appearance components, and employ 2D Gaussian splatting for fast, di erentiable rendering. This formulation enables e cient tting of strokes to the input while jointly optimizing control points and brush texture.We further demonstrate promising results on video by incorporating temporal tracking and adaptive keyframe introduction. 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 tting is highly compatible with user input, through the correction of the splines and the selection of optimization parameters.
https://web.archive.org/web/20260815004033/https://studios.d...
Most of the content is in the PDF: https://studios.disneyresearch.com/app/uploads/2026/07/2D-Ga...The page itself when loaded from the original link contains the abstract and then a link to the PDF, just like in the snapshot you linked.
https://x.com/andrew_n_carr/status/2087937511171690592