Photorealistic Facial Wrinkles Removal

TL;DR

This paper presents a novel two-stage method for photorealistic facial wrinkle removal using segmentation and inpainting, achieving state-of-the-art results and introducing a new high-resolution dataset for wrinkle detection evaluation.

Contribution

It introduces a new two-stage approach combining wrinkle segmentation and inpainting with a novel loss, and provides the first high-resolution dataset for wrinkle detection evaluation.

Findings

Achieves unprecedented realism in wrinkle removal.

Outperforms existing methods quantitatively and qualitatively.

Introduces the FFHQ-Wrinkles dataset for evaluation.

Abstract

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.