  Aiming at developing intuitive and easy-to-use portrait editing tools, we
propose a novel vectorization method that can automatically convert raster
images into a 3-tier hierarchical representation. The base layer consists of a
set of sparse diffusion curves (DC) which characterize salient geometric
features and low-frequency colors and provide means for semantic color transfer
and facial expression editing. The middle level encodes specular highlights and
shadows to large and editable Poisson regions (PR) and allows the user to
directly adjust illumination via tuning the strength and/or changing shape of
PR. The top level contains two types of pixel-sized PRs for high-frequency
residuals and fine details such as pimples and pigmentation. We also train a
deep generative model that can produce high-frequency residuals automatically.
Thanks to the meaningful organization of vector primitives, editing portraits
becomes easy and intuitive. In particular, our method supports color transfer,
facial expression editing, highlight and shadow editing and automatic
retouching. Thanks to the linearity of the Laplace operator, we introduce alpha
blending, linear dodge and linear burn to vector editing and show that they are
effective in editing highlights and shadows. To quantitatively evaluate the
results, we extend the commonly used FLIP metric (which measures differences
between two images) by considering illumination. The new metric, called
illumination-sensitive FLIP or IS-FLIP, can effectively capture the salient
changes in color transfer results, and is more consistent with human perception
than FLIP and other quality measures on portrait images. We evaluate our method
on the FFHQR dataset and show that our method is effective for common portrait
editing tasks, such as retouching, light editing, color transfer and expression
editing. We will make the code and trained models publicly available.
