Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction

• URL: http://arxiv.org/abs/2112.14382v1
• Date: Wed, 29 Dec 2021 03:30:50 GMT
• Title: Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
• Authors: Hitika Tiwari, Min-Hung Chen, Yi-Min Tsai, Hsien-Kai Kuo, Hung-Jen Chen, Kevin Jou, K. S. Venkatesh, Yong-Sheng Chen
• Abstract summary: We propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images. The proposed network contains 1) the Guidance Pipeline to obtain the 3D face coefficients for the clean faces, and 2) the Robustification Pipeline to acquire the consistency between the estimated coefficients for occluded or noisy images and the clean counterpart. On the three variations of the test dataset of CelebA, our method outperforms the current state-of-the-art method by large margins.
• Score: 14.203990541030445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the recent developments in 3D Face Reconstruction from occluded and noisy face images, the performance is still unsatisfactory. One of the main challenges is to handle moderate to heavy occlusions in the face images. In addition, the noise in the face images inhibits the correct capture of facial attributes, thus needing to be reliably addressed. Moreover, most existing methods rely on additional dependencies, posing numerous constraints over the training procedure. Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images. The proposed network contains 1) the Guidance Pipeline to obtain the 3D face coefficients for the clean faces, and 2) the Robustification Pipeline to acquire the consistency between the estimated coefficients for occluded or noisy images and the clean counterpart. The proposed image- and feature-level loss functions aid the ROGUE learning process without posing additional dependencies. On the three variations of the test dataset of CelebA: rational occlusions, delusional occlusions, and noisy face images, our method outperforms the current state-of-the-art method by large margins (e.g., for the shape-based 3D vertex errors, a reduction from 0.146 to 0.048 for rational occlusions, from 0.292 to 0.061 for delusional occlusions and from 0.269 to 0.053 for the noise in the face images), demonstrating the effectiveness of the proposed approach.

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