InverseFaceNet: Deep Monocular Inverse Face Rendering

TY - GEN

T1 - InverseFaceNet: Deep Monocular Inverse Face Rendering

AU - Kim, Hyeongwoo

AU - Zollhöfer, Michael

AU - Tewari, Ayush

AU - Thies, Justus

AU - Richardt, Christian

AU - Theobalt, Christian

PY - 2018/6/18

Y1 - 2018/6/18

N2 - 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.

AB - 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.

UR - http://richardt.name/publications/inversefacenet/

U2 - 10.1109/CVPR.2018.00486

DO - 10.1109/CVPR.2018.00486

M3 - Conference contribution

T3 - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

SP - 4625

EP - 4634

BT - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

PB - IEEE

ER -