Face Depth Estimation With Conditional Generative Adversarial Networks

Abstract

Depth map estimation and 3-D reconstruction from a single or a few face images is an important research field in computer vision. Many approaches have been proposed and developed over the last decade. However, issues like robustness are still to be resolved through additional research. With the advent of the GPU computational methods, convolutional neural networks are being applied to many computer vision problems. Later, conditional generative adversarial networks (CGAN) have attracted attention for its easy adaptation for many picture-to-picture problems. CGANs have been applied for a wide variety of tasks, such as background masking, segmentation, medical image processing, and superresolution. In this work, we developed a GAN-based method for depth map estimation from any given single face image. Many variants of GANs have been tested for the depth estimation task for this work. We conclude that conditional Wasserstein GAN structure offers the most robust approach. We have also compared the method with other two state-of-the-art methods based on deep learning and traditional approaches and experimentally shown that the proposed method offers great opportunities for estimation of face depth maps from face images.