  Editing and retouching facial attributes is a complex task that usually
requires human artists to obtain photo-realistic results. Its applications are
numerous and can be found in several contexts such as cosmetics or digital
media retouching, to name a few. Recently, advancements in conditional
generative modeling have shown astonishing results at modifying facial
attributes in a realistic manner. However, current methods are still prone to
artifacts, and focus on modifying global attributes like age and gender, or
local mid-sized attributes like glasses or moustaches. In this work, we revisit
a two-stage approach for retouching facial wrinkles and obtain results with
unprecedented realism. First, a state of the art wrinkle segmentation network
is used to detect the wrinkles within the facial region. Then, an inpainting
module is used to remove the detected wrinkles, filling them in with a texture
that is statistically consistent with the surrounding skin. To achieve this, we
introduce a novel loss term that reuses the wrinkle segmentation network to
penalize those regions that still contain wrinkles after the inpainting. We
evaluate our method qualitatively and quantitatively, showing state of the art
results for the task of wrinkle removal. Moreover, we introduce the first
high-resolution dataset, named FFHQ-Wrinkles, to evaluate wrinkle detection
methods.
