2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper ID3D-4.9
Paper Title REAL-TIME 3D FACE RECONSTRUCTION FROM SINGLE IMAGE USING END-TO-END CNN REGRESSION
Authors Shan Wang, Xukun Shen, Kun Yu, Beihang University, China
Session3D-4: 3D Image and Video Processing
LocationArea J
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Three-Dimensional Image and Video Processing: Image and video processing augmented and virtual reality
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a learning-based method for detailed 3D face reconstruction from a single unconstrained image. The core of our method is an end-to-end multi-task network architecture. The purpose of the proposed network is to predict a geometric representation of 3D face from a given facial image. Unlike most existing reconstruction methods using low-dimension morphable models, we propose a pixel-based multi-scale representation of a detailed 3D face to ensure that our reconstruction results are not limited by the expressiveness of linear models. We break the task of high-fidelity face reconstruction into three subtasks, which are face region segmentation, coarse-scale reconstruction and detail recovery. So the end-to-end network is constructed as a multi-task mode, which contains three subtask networks to deal with different subtasks respectively. A backbone network with feature pyramid structure is proposed as well to provide different levels of feature maps required by the three subtask networks. We train our end-to-end network in the spirit of the recent photo-realistic data generation approach. The experimental results demonstrate that our method can work with totally unconstrained images and produce high-quality reconstruction but with less runtime compared to the state-of-the-art.