DECA: Learning an Animatable Detailed 3D Face Model from In-the-Wild Images
2021-05-03
DECA reconstructs a 3D head model with detailed facial geometry from a single input image. The resulting 3D head model can be easily animated. The main features: * Reconstruction: produces head pose, shape, detailed face geometry, and lighting information from a single image. * Animation: animate the face with realistic wrinkle deformations. * Robustness: tested on facial images in unconstrained conditions. Our method is robust to various poses, illuminations and occlusions. * Accurate: state-of-the-art 3D face shape reconstruction on the NoW Challenge benchmark dataset.
DECA reconstructs a 3D head model with detailed facial geometry from a single input image. The resulting 3D head model can be easily animated.
The main features:
- Reconstruction: produces head pose, shape, detailed face geometry, and lighting information from a single image.
- Animation: animate the face with realistic wrinkle deformations.
- Robustness: tested on facial images in unconstrained conditions. Our method is robust to various poses, illuminations and occlusions.
- Accurate: state-of-the-art 3D face shape reconstruction on the NoW Challenge benchmark dataset.
| Author(s): | Yao Feng, Haiwen Feng, Michael Black, Timo Bolkart |
| Department(s): |
Perceiving Systems |
| Publication(s): |
Learning an Animatable Detailed {3D} Face Model from In-the-Wild Images
|
| Authors: | Yao Feng, Haiwen Feng, Michael Black, Timo Bolkart |
| Release Date: | 2021-05-03 |
| Repository: | https://github.com/YadiraF/DECA |
