3D Face Reconstruction Based on a Single Image: A Review

Abstract

Nowadays, along with the rise of digital human system, 3D animation, intelligent medical and other industries, 3D face reconstruction technology has become a popular research direction in computer vision and computer graphics. Traditional 3D face reconstruction techniques are affected by face expression, occlusion, and ambient light, resulting in poor accuracy and robustness of the reconstructed model, etc. With the rise of deep learning, all of the above problems have been greatly improved. Focusing on 3D face reconstruction techniques based on deep learning, this paper categorizes the existing research works into 3D face reconstruction based on hybrid learning and explicit regression. The first category of research work fits 2D faces to 3D models, which is a pathological process that requires solving the basis vector coefficients of the 3D face statistical model. The second type of research work, instead of Model Fitting, represents 3D faces with multiple data types in the display space and directly regresses 2D faces through deep networks. This review provides the latest advances in single-image-based 3D face reconstruction techniques in recent years, summarizing some commonly used face datasets, evaluation metrics, and applications. Finally, we discuss the main challenges and future trends of the single-image 3D face reconstruction task.

Keywords

• 3D face reconstruction
• deep learning
• 3DMM
• model fitting
• Nerf