Abstract
3D morphable models (3DMMs) are generative models for face shape and appearance. Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent. However, the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution. In contrast, the identity embeddings meet the hypersphere distribution, and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously. In other words, recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution. To address this issue, we propose the Sphere Face Model (SFM), a novel 3DMM for monocular face reconstruction, preserving both shape fidelity and identity consistency. The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes, and the basic matrix is learned by adopting a two-stage training approach where 3D and 2D training data are used in the first and second stages, respectively. We design a novel loss to resolve the distribution mismatch, enforcing that the shape parameters have the hyperspherical distribution. Our model accepts 2D and 3D data for constructing the sphere face models. Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space. Moreover, it produces high-fidelity face shapes consistently in challenging conditions in monocular face reconstruction. The code will be released at https://github.com/a686432/SIR
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Acknowledgements
The research is supported in part by National Natural Science Foundation of China (61972342, 61832016), Science and Technology Department of Zhejiang Province (2018C01080), Zhejiang Province Public Welfare Technology Application Research (LGG22F020009), Key Laboratory of Film and TV Media Technology of Zhejiang Province (2020E10015), and Teaching Reform Project of Communication University of Zhejiang (jgxm202131).
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Diqiong Jiang is a Ph.D. student under the guidance of Prof. Ruofeng Tong in the College of Computer Science, Zhejiang University. He received his B.S. degree from the College of Electronic and Information Engineering, Shandong University in 2017.
Yiwei Jin is a Ph.D. student under the guidance of Prof. Ruofeng Tong in the College of Computer Science, Zhejiang University. She received her B.S. degree from the College of Digital Media, Nanchang University
Fang-Lue Zhang earns his doctoral degree working on structure-based image analysis and editing at the Visual Media Lab at Tsinghua University in 2015, and continued working as a post-doctoral researcher and collaborating on research projects with industry, where his research focus was mainly on the intelligent image and video editing and enhancement. In 2017, he joined the Victoria Graphics Group as a lecturer. His current research interests include deep learning based image/video manipulation approaches and panoramic 360 image processing for virtual reality applications. He is also working on a SfTI Robotics Spearhead project, where he focuses on providing efficient deep learning method for RGB-D object recognition and segmentation.
Zhe Zhu is currently a research engineer at Samsung Research America. Before that he has worked at MathWorks and Duke University. He got his Ph.D. degree in the Department of Computer Science and Technology, Tsinghua University, in 2017. He received his bachelor degree in Wuhan University in 2011. His research interests are in computer vision and computer graphics.
Yun Zhang is now an associate professor in Communication University of Zhejiang (Hangzhou, China). He received his Ph.D. degree in computer science and technology from Zhejiang University (Hangzhou, China), under the guidance of Prof. Ruofeng Tong in 2013. Before that, he received his B.S. and M.S. degrees in computer science from Hangzhou Dianzi University (Hangzhou, China), in 2006 and 2009 respectively. He visited the Computational Media Innovation Centre (CMIC) of Victoria University of Wellington from July to August in 2019, worked with Dr. Fang-Lue Zhang from February to August in 2018, and visited Visual Computing Group of Cardiff University, supervised by Prof. Yu-Kun Lai. During his Ph.D. study, he visited the Graphics and Geometric Computing Group of Tsinghua University during 2010–2012, supervised by Prof. Shi-Min Hu.
Ruofeng Tong is a professor and doctoral supervisor of the College of Computer Science, Zhejiang University. Moreover, he is a deputy director of graphics big data special committee of China Graphics Society, and coeditor of the international journal CIS. He has been engaged in the research of computer vision, computer graphics, and computer-aided design for many years, and has undertaken the research of national 863, 973 scientific program and National Natural Science Foundation of China
Min Tang is currently a professor of the College of Computer Science at Zhejiang University, China. He received his B.S., M.S., and Ph.D. degrees from Zhejiang University in 1994, 1996, and 1999, respectively. From June 2003 to May 2004, he was a visiting scholar at Wichita State University. From April 2007 to April 2008, and September 2016 to April 2017, he was a visiting scholar at the University of North Carolina at Chapel Hill. His research interests include physically-based simulation and GPU-based computation acceleration. He has published more than 90 journal and conference papers.
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Jiang, D., Jin, Y., Zhang, FL. et al. Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training. Comp. Visual Media 9, 279–296 (2023). https://doi.org/10.1007/s41095-022-0286-4
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DOI: https://doi.org/10.1007/s41095-022-0286-4