Due to the global outbreak of coronaviruses, people are increasingly wearing masks even when photographed. As a result, photos uploaded to web pages and social networking services with the lower half of the face hidden are less likely to convey the attractiveness of the photographed persons. In this study, we propose a method to complete facial mask regions using StyleGAN2, a type of Generative Adversarial Networks (GAN). In the proposed method, a reference image of the same person without a mask is prepared separately from a target image of the person wearing a mask. After the mask region in the target image is temporarily inpainted, the face orientation and contour of the person in the reference image are changed to match those of the target image using StyleGAN2. The changed image is then composited into the mask region while correcting the color tone to produce a mask-free image while preserving the person's features.

This paper proposes a novel approach to generate stereo video in which the zoom magnification is not constant. Although this has been achieved mechanically in a conventional way, it is necessary for this approach to develop a mechanically complex system for each stereo camera system. Instead of a mechanical solution, we employ an approach from the software side: by using a pair of zoomed and non-zoomed video, a part of the non-zoomed video image is cut out and super-resolved for generating stereo video without a special hardware. To achieve this, (1) the zoom magnification parameter is automatically determined by using distributions of intensities, and (2) the cutout image is super-resolved by using optically zoomed images as exemplars. The effectiveness of the proposed method is quantitatively and qualitatively validated through experiments.