Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization

Ye Chen, Bingbing Ni, Jinfan Liu, Xiaoyang Huang, Xuanhong Chen

Abstract: We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way, particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D control points. On one hand, these points constitute a collection of paramet-ric/vectorized geometric primitives (e.g., curves and closed shapes) describing the shape characteristics of the target image. On the other hand, local texture codes, in terms of implicit neural network parameters, are spatially dis-tributed into each control point, yielding local coordinate-to-RGB mappings within the anchored region of each con-trol point. In the meantime, a zero-shot learning algorithm is developed to decompose an arbitrary raster image into the above representation, for the sake of high-fidelity im-age vectorization with convenient editing ability. Extensive experiments on a series of image vectorization and editing tasks well demonstrate the high accuracy offered by our proposed method, with a significantly higher image com-pression ratio over prior art.