  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.
