Date: November 2020.
Source: 2020 International Conference on 3D Vision (3DV), 2020, pp. 858-867, doi: 10.1109/3DV50981.2020.00096.
Abstract: We present an approach to efficiently learn an accurate and complete 3D face model from a single image. Previous methods heavily rely on 3D Morphable Models to populate the facial shape space as well as an over-simplified shading model for image formulation. By contrast, our method directly augments a large set of 3D faces from a compact collection of facial scans and employs a high-quality rendering engine to synthesize the corresponding photo-realistic facial images. We first use a deep neural network to regress vertex coordinates from the given image and then refine them by a non-rigid deformation process to more accurately capture local shape similarity. We have conducted extensive experiments to demonstrate the superiority of the proposed approach on 2D-to-3D facial shape inference, especially its excellent generalization property on real-world selfie images.

Article: Learning 3D Faces from Photo-Realistic Facial Synthesis.
Authors: R Wang, C-F. Chen, H Peng, X Liu, X Li. Oben, Inc.