Image rasterization is a mature technique in computer graphics, while image
vectorization, the reverse path of rasterization, remains a major challenge.
Recent advanced deep learning-based models achieve vectorization and semantic
interpolation of vector graphs and demonstrate a better topology of generating
new figures. However, 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 convenient 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 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 experiments demonstrate that
LIVE presents more plausible vectorized forms than prior works and can be
generalized to new images. With the help of this newly learned topology, LIVE
initiates human editable SVGs for both designers and other downstream
applications. Codes are made available at
this https URL

本文提出了一种名为 LIVE 的图像逐层矢量化方法，它能够将光栅图像转换为 SVG，并同时保持其图像拓扑结构，通过新的贝塞尔路径，新设计的损失函数以及逐元件路径初始化技术来优化路径，实验结果表明，LIVE 比以前的工作呈现更有说服力的矢量形式，并且可以推广到新的图像。