Yiqian Wu, Yongliang Yang, Qinjie Xiao, Xiaogang Jin
Given a portrait image (top) with a double chin as input, our method can automatically generate a new portrait without a double chin (bottom). The proposed approach is able to preserve facial identity thanks to the seamless integration of the new chin with the original face through editing the semantic latent code in the StyleGAN latent space.
Facial structure
editing of portrait images is challenging given the facial variety, the lack
of ground-truth, the necessity of jointly adjusting color and shape, and the
requirement of no visual artifacts. In this paper, we investigate how to
perform chin editing as a case study of editing facial structures. We
present a novel method that can automatically remove the double chin effect
in portrait images. Our core idea is to train a fine classification boundary
in the latent space of the portrait images. This can be used to edit the
chin appearance by manipulating the latent code of the input portrait image
while preserving the original portrait features. To achieve such a fine
separation boundary, we employ a carefully designed training stage based on
latent codes of paired synthetic images with and without a double chin. In
the testing stage, our method can automatically handle portrait images with
only a refinement to subtle misalignment before and after double chin
editing. Our model enables alteration to the neck region of the input
portrait image while keeping other regions unchanged, and guarantees the
rationality of neck structure and the consistency of facial characteristics.
To the best of our knowledge, this presents the first effort towards an
effective application for editing double chins. We validate the efficacy and
efficiency of our approach through extensive experiments and user studies.