Self-supervised Multi-level Face Model Learning
for Monocular Reconstruction at over 250 Hz
Abstract
The reconstruction of dense 3D models of face geometry and appearance from a single image is highly challenging and ill-posed. To constrain the problem, many approaches rely on strong priors, such as parametric face
models learned from limited 3D scan data. However, prior
models restrict generalization of the true diversity in facial geometry, skin reflectance and illumination. To alleviate this problem, we present the first approach that jointly
learns 1) a regressor for face shape, expression, reflectance
and illumination on the basis of 2) a concurrently learned
parametric face model. Our multi-level face model combines the advantage of 3D Morphable Models for regularization with the out-of-space generalization of a learned
corrective space. We train end-to-end on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and
a self-supervised training loss, both defined at multiple detail levels. Our approach compares favorably to the stateof-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.