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Given a monocular face image as input, 3D face geometry reconstruction aimsto recover a corresponding 3D face mesh. Recently, both optimization-based andlearning-based face reconstruction methods have taken advantage of the emergingdifferentiable renderer and shown promising results. However, thedifferentiable renderer, mainly based on graphics rules, simplifies therealistic mechanism of the illumination, reflection, \etc, of the real world,thus cannot produce realistic images. This brings a lot of domain-shift noiseto the optimization or training process. In this work, we introduce a novelGenerative Adversarial Renderer (GAR) and propose to tailor its invertedversion to the general fitting pipeline, to tackle the above problem.Specifically, the carefully designed neural renderer takes a face normal mapand a latent code representing other factors as inputs and renders a realisticface image. Since the GAR learns to model the complicated real-world image,instead of relying on the simplified graphics rules, it is capable of producingrealistic images, which essentially inhibits the domain-shift noise in trainingand optimization. Equipped with the elaborated GAR, we further proposed a novelapproach to predict 3D face parameters, in which we first obtain fine initialparameters via Renderer Inverting and then refine it with gradient-basedoptimizers. Extensive experiments have been conducted to demonstrate theeffectiveness of the proposed generative adversarial renderer and the noveloptimization-based face reconstruction framework. Our method achievesstate-of-the-art performances on multiple face reconstruction datasets.

Inverting Generative Adversarial Renderer for Face Reconstruction