Abstract
The last few years have witnessed the great success of generative adversarial networks (GANs) in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction works often pursue higher resolutions and ignore occlusion. We study the problem of detailed 3D facial reconstruction under occluded scenes. This is a challenging problem; currently, the collection of such a large scale high resolution 3D face dataset is still very costly. In this work, we propose a deep learning based approach for detailed 3D face reconstruction that does not require large-scale 3D datasets. Motivated by generative face image inpainting and weakly-supervised 3D deep reconstruction, we propose a complete 3D face model generation method guided by the contour. In our work, the 3D reconstruction framework based on weak supervision can generate convincing 3D models. We further test our method on the MICC, Florence and LFW datasets, showing its strong generalization capacity and superior performance.
Highlights
Single-view 3D face reconstruction refers to obtaining a user-specific 3D face surface model given one input face image
We propose a novel approach that combines the face parsing approach and face contour map to generate a face with complete facial features
In response to the problem of an invisible face area under occluded scenes, we propose synthesizing the input face image based on GANS rather than reconstructing the 3D face directly
Summary
Single-view 3D face reconstruction refers to obtaining a user-specific 3D face surface model given one input face image. This is a classical and fundamental problem in computer vision [1,2,3]. It has a wide range of applications, such as 3D-assisted face recognition [1,4,5,6] and digital entertainment [7]. These methods can only work effectively when frontal faces are unobstructed, which makes the application of scenes very limited. When considering the occlusion of the scene, the reconstruction of the 3D face model is challenging since part of the facial features is not visible
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
