  Despite the recent developments in 3D Face Reconstruction from occluded and
noisy face images, the performance is still unsatisfactory. 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. To facilitate model evaluation, we
propose two challenging occlusion face datasets, ReaChOcc and SynChOcc,
containing real-world and synthetic occlusion-based face images for robustness
evaluation. Also, a noisy variant of the test dataset of CelebA is produced for
evaluation. Our method outperforms the current state-of-the-art method by large
margins (e.g., for the perceptual errors, a reduction of 23.8% for real-world
occlusions, 26.4% for synthetic occlusions, and 22.7% for noisy images),
demonstrating the effectiveness of the proposed approach. The occlusion
datasets and the corresponding evaluation code are released publicly at
https://github.com/ArcTrinity9/Datasets-ReaChOcc-and-SynChOcc.
