Towards Layer-Wise Image Vectorization

Abstract

Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge. Recent ad- vanced deep learning-based models achieve vectorization and semantic interpolation of vector graphs and demon- strate a better topology of generating new figures. How- ever, deep models cannot be easily generalized to out-of- domain testing data. The generated SVGs also contain complex and redundant shapes that are not quite conve- nient for further editing. Specifically, the crucial layer- wise topology and fundamental semantics in images are still not well understood and thus not fully explored. In this work, we propose Layer-wise Image Vectorization, namely LIVE, to convert raster images to SVGs and simultaneously maintain its image topology. LIVE can generate compact SVG forms with layer-wise structures that are semantically consistent with the human perspective. We progressively add new bezier paths and optimize these paths with the layer-wise framework, newly designed loss functions, and component-wise path initialization technique. Our experi- ments demonstrate that LIVE presents more plausible vec- torized forms than prior works and can be generalized to new images. With the help of this newly learned topol- ogy, LIVE initiates human editable SVGs for both design- ers and other downstream applications. Codes are made available at https://github.com/Picsart-AI-Research/LIVE- Layerwise-Image-Vectorization.