Corpus ID: 235458378

To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision

@article{Li2021ToFO,
  title={To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision},
  author={Chunlu Li and Andreas Morel-Forster and T. Vetter and B. Egger and Adam Kortylewski},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09614}
}
3D face reconstruction from a single image is challenging due to its ill-posed nature. Model-based face autoencoders address this issue effectively by fitting a face model to the target image in a weakly supervised manner. However, in unconstrained environments occlusions distort the face reconstruction because the model often erroneously tries to adapt to occluded face regions. Supervised occlusion segmentation is a viable solution to avoid the fitting of occluded face regions, but it requires… Expand

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