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3D face dense reconstruction based on sparse points using probabilistic principal component analysis

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Abstract

Reconstructing 3D face from sparse points is an ill-posed problem. While there already exits available solutions addressing this problem, to our knowledge, we propose a better-performed approach which can robustly reconstruct fine 3D face shape. Our method includes two modules: face model establishment based on probabilistic principal component analysis (PPCA) trained in an unsupervised manner to learn transformation between landmarks and point cloud in their low-dimensional representation, and 3D face reconstruction based on learned relation between them to reconstruct fine face shape. Overall, our method considers the probability of face shape and learns more useful information of 3D face shape. We compare our method with 3 typical and state-of-the-art methods on 2 datasets and the effectiveness of our method is demonstrated generally. Further experiments on datasets with noise of different intensities show the stability of our method.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (No.61972041, No.62072045), the National Key Cooperation between the BRICS of China (No.2017YFE0100500), National Key R&D Program of China (No.2017YFB1002604, No.2017YFB1402105).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Beijing, China

    Xiaoxiao Xie, Xingce Wang & Zhongke Wu

  2. Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing Normal University, 100875, Beijing, China

    Xiaoxiao Xie, Xingce Wang & Zhongke Wu

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  1. Xiaoxiao Xie
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  2. Xingce Wang
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  3. Zhongke Wu
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Correspondence to Zhongke Wu.

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Xie, X., Wang, X. & Wu, Z. 3D face dense reconstruction based on sparse points using probabilistic principal component analysis. Multimed Tools Appl 81, 2937–2957 (2022). https://doi.org/10.1007/s11042-021-11707-0

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