Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

Soubhik Sanyal, Timo Bolkart, Haiwen Feng, Michael J. Black
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Figure 1: Without 3D supervision, RingNet learns a mapping from the pixels of a single image to the 3D facial parameters of the FLAME model [21]. Top: Images are from the CelebA dataset [22]. Bottom: estimated shape, pose and expression.
doi:10.1109/cvpr.2019.00795 dblp:conf/cvpr/SanyalBFB19 fatcat:bskyym6j2vewbmt372rxob5n5q