Abstract
How to accurately reconstruct the 3D model human face is a challenge issue in the computer vision. Due to the complexity of face reconstruction and diversity of face features, most existing methods are aimed at reconstructing a smooth face model with ignoring face details. In this paper a novel deep learning‐based face reconstruction method is proposed. It contains two modules: initial face reconstruction and face details synthesis. In the initial face reconstruction module, a neural network is used to detect the facial feature points and the angle of the pose face, and 3D Morphable Model (3DMM) is used to reconstruct the rough shape of the face model. In the face detail synthesis module, Conditional Generation Adversarial Network (CGAN) is used to synthesize the displacement map. The map provides texture features to render to the face surface reconstruction, so as to reflect the face details. Our proposal is evaluated by Facescape dataset in experiments and achieved better performance than other current methods.
Highlights
Face is one of the most important biological characteristics of human beings, and face modeling is often used in security, animation, biometrics, and other fields [1, 2]
The reconstruction system is divided into two parts: the initial face reconstruction module and the face detail synthesis module, and both are based on deep learning [3]
We propose a reconstruction system for face model
Summary
Face is one of the most important biological characteristics of human beings, and face modeling is often used in security, animation, biometrics, and other fields [1, 2]. The steps of 3D face reconstruction are very complex if it is reconstructed step by step This reconstruction model will lead to more data loss and less accuracy. The supervised learning method is used to train 60K face images from 300W-LP dataset to obtain the corresponding dictionary. In this process, a CNN network is used to align the negative faces and detect their feature points. The face detail synthesis module is based on CGAN, which inputs the original image to synthesize the displacement map, and the displacement map retains the more complete details of the face [5]. The face detail synthesis module refers to DFDN to train high-quality images and get the training data, which can synthesize the displacement map from the original image
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.
