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Computational Visual Media 2023, 9(2): 279-296 https://doi.org/10.1007/s41095-022-0286-4
Research Article | Open Access | Issue | Published: 03 January 2023

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Show Author's Information Diqiong Jiang1,  Yiwei Jin1,  Fang-Lue Zhang2,  Zhe Zhu3,  Yun Zhang4,  Ruofeng Tong1( ),  Min Tang1
Zhejiang University, Hangzhou 310058, China
Victoria University of Wellington, Wellington 6012, New Zealand
Duke University, Durham, North Carolina 27708, USA
Communication University of Zhejiang, Hangzhou 310019, China
Keywords:
deep learning, facial modeling, face recon-struction, 3D morphable model (3DMM)
Cite this article:
Jiang D, Jin Y, Zhang F-L, et al. Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training. Computational Visual Media, 2023, 9(2): 279-296. https://doi.org/10.1007/s41095-022-0286-4
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Abstract Full text About this article

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, theshape 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 2Dand 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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About this article

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Show Author's information Diqiong Jiang1, Yiwei Jin1, Fang-Lue Zhang2, Zhe Zhu3, Yun Zhang4, Ruofeng Tong1( ), Min Tang1
Zhejiang University, Hangzhou 310058, China
Victoria University of Wellington, Wellington 6012, New Zealand
Duke University, Durham, North Carolina 27708, USA
Communication University of Zhejiang, Hangzhou 310019, China

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, theshape 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 2Dand 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

Keywords: deep learning, facial modeling, face recon-struction, 3D morphable model (3DMM)

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Received: 10 January 2022
Accepted: 05 April 2022
Published: 03 January 2023
Issue date: June 2023

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Acknowledgements

The research is supported in part by National NaturalScience Foundation of China (61972342, 61832016), Scienceand 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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