InverseFaceNet: Deep Monocular Inverse Face Rendering

2018

Conference Paper

ncs

We introduce InverseFaceNet, a deep convolutional inverse rendering framework for faces that jointly estimates facial pose, shape, expression, reflectance and illumination from a single input image. By estimating all parameters from just a single image, advanced editing possibilities on a single face image, such as appearance editing and relighting, become feasible in real time. Most previous learning-based face reconstruction approaches do not jointly recover all dimensions, or are severely limited in terms of visual quality. In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created training corpus. Our approach builds on a novel loss function that measures model-space similarity directly in parameter space and significantly improves reconstruction accuracy. We further propose a self-supervised bootstrapping process in the network training loop, which iteratively updates the synthetic training corpus to better reflect the distribution of real-world imagery. We demonstrate that this strategy outperforms completely synthetically trained networks. Finally, we show high-quality reconstructions and compare our approach to several state-of-the-art approaches.

@inproceedings{InverseFaceNet,
  title = {InverseFaceNet: Deep Monocular Inverse Face Rendering},
  author = {Kim, Hyeongwoo and Zollh{\"o}fer, Michael and Tewari, Ayush and Thies, Justus and Richardt, Christian and Theobalt, Christian},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  doi = {},
  url = {https://justusthies.github.io/posts/inversefacenet/}
}