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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 estimatingmore robust and accurate geometry, relatively little attention has been paid toimproving the quality of the texture model. Meanwhile, with the advent ofGenerative Adversarial Networks (GANs), there has been great progress inreconstructing realistic 2D images. Recent work demonstrates that GANs trainedwith abundant high-quality UV maps can produce high-fidelity textures superiorto those produced by existing methods. However, acquiring such high-quality UVmaps is difficult because they are expensive to acquire, requiring laboriousprocesses to refine. In this work, we present a novel UV map generative modelthat learns to generate diverse and realistic synthetic UV maps withoutrequiring high-quality UV maps for training. Our proposed framework can betrained solely with in-the-wild images (i.e., UV maps are not required) byleveraging a combination of GANs and a differentiable renderer. Bothquantitative and qualitative evaluations demonstrate that our proposed texturemodel produces more diverse and higher fidelity textures compared to existingmethods.

StyleUV: Diverse and High-fidelity UV Map Generative Model