  Reconstructing 3D human faces in the wild with the 3D Morphable Model (3DMM)
has become popular in recent years. While most prior work focuses on estimating
more robust and accurate geometry, relatively little attention has been paid to
improving the quality of the texture model. Meanwhile, with the advent of
Generative Adversarial Networks (GANs), there has been great progress in
reconstructing realistic 2D images. Recent work demonstrates that GANs trained
with abundant high-quality UV maps can produce high-fidelity textures superior
to those produced by existing methods. However, acquiring such high-quality UV
maps is difficult because they are expensive to acquire, requiring laborious
processes to refine. In this work, we present a novel UV map generative model
that learns to generate diverse and realistic synthetic UV maps without
requiring high-quality UV maps for training. Our proposed framework can be
trained solely with in-the-wild images (i.e., UV maps are not required) by
leveraging a combination of GANs and a differentiable renderer. Both
quantitative and qualitative evaluations demonstrate that our proposed texture
model produces more diverse and higher fidelity textures compared to existing
methods.
