Open Access3D Face Reconstruction Based on A Single Image: A Review
Author(s) -
Haojie. Diao,
Xingguo. Jiang,
Yang. Fan,
Ming. Li,
Hongcheng. Wu
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3381975
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.