Self-supervised single-image 3D face reconstruction method based on attention mechanism and attribute refinement

Single-view 3D face reconstruction refers to recovering 3D information of a face, such as shape and texture, from a single image. With the wide application of deep learning in the image field, there have been a number of researches using this method to learn the 3D shape and texture of a face from image information. In this paper, we propose a self-supervised single-image 3D face reconstruction method based on the attention mechanism and attribute refinement, which incorporates the attention mechanism in the network structural model, allowing feature extraction to fuse the information of the channel domain and the spatial domain to enhance the feature extraction capability. Joint 2D image-level supervision and supervision between 3D attributes can better learn the 3D model of the face. In this paper, on the basis of using the traditional 2D image supervision, we design a variety of loss functions by combining the cyclic consistency, interpolation consistency, and landmark consistency to realize the 3D attribute level supervision. In order to strengthen the ability to characterize the details of the face, this paper proposes an attribute refinement network to enhance the ability of the model to reconstruct the details and make the reconstruction results more realistic. Based on the symmetry of the face, this paper constructs a deep learning network model to decouple the 3D information directly from the image, and finally realizes unsupervised 3D face reconstruction from a single image.