Feasibility Analysis of Deep Learning-Based Reality Assessment of Human Back-View Images

Abstract

Realistic personalized avatars can play an important role in social interactions in virtual reality, increasing body ownership, presence, and dominance. A simple way to obtain the texture of an avatar is to use a single front-view image of a human and to generate the hidden back-view image. The realism of the generated image is crucial in improving the overall texture quality, and subjective image quality assessment methods can play an important role in the evaluation. The subjective methods, however, require dozens of human assessors, a controlled environment, and time. This paper proposes a deep learning-based image reality assessment method, which is fully automatic and has a short testing time of nearly a quarter second per image. We train various discriminators to predict whether an image is real or generated. The trained discriminators are then used to give a mean opinion score for the reality of an image. Through experiments on human back-view images, we show that our learning-based mean opinion scores are close to their subjective counterparts in terms of the root mean square error between them.