![]() ![]() In this paper, we propose a novel self-supervised learning framework for reconstructing high-quality 3D faces from single-view images in-the-wild. Although each method has its own advantage, none of them is capable of recovering a high-fidelity and re-renderable facial texture, where the term 're-renderable' demands the facial texture to be spatially complete and disentangled with environmental illumination. The most recent works tackle facial texture reconstruction problem by applying either generation-based or reconstruction-based methods. ![]() Reconstructing high-fidelity 3D facial texture from a single image is a challenging task since the lack of complete face information and the domain gap between the 3D face and 2D image. The review also identifies current gaps and suggests avenues for future research. ![]() Unlike the other two strategies, photometry-based methods have decreased in number since the required strong assumptions cause the reconstructions to be of more limited quality than those resulting from model fitting and deep learning methods. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, matching its extension to that of the widespread statistical model fitting. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a comprehensive list of the publicly available 3D facial models. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In this work, we review 3D face reconstruction methods in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. ![]() Despite providing a more accurate representation of the face, 3D face images are more complex to acquire than 2D pictures. Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. ![]()
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