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 parametric/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 distributed into each control point, yielding local coordinateto-RGB mappings within the anchored region of each control 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 image 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 compression ratio over prior art.