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Figure 1 . We propose multi-frame self-supervised training of a deep network based on in-the-wild video data for jointly learning a face model and 3D face reconstruction. Our approach successfully disentangles facial shape, appearance, expression, and scene illumination. Abstract Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existingdoi:10.1109/cvpr.2019.01107 dblp:conf/cvpr/TewariB0BESPZT19 fatcat:6gf5b75bkzbldhzbyqnun4okzm