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 state-of-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.

CVPR 2018 (Oral). Project webpage: https://gvv.mpi-inf.mpg.de/projects/FML/

References in corpus (1)

• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (5)

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• Extreme 3D Face Reconstruction: Seeing Through Occlusions
• Robust Point Light Source Estimation Using Differentiable Rendering
• A Decoupled 3D Facial Shape Model by Adversarial Training
• A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction