InverseFaceNet: Deep Monocular Inverse Face Rendering

Understand

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.