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Inferring 3D object structures from a single image is an ill-posed task dueto depth ambiguity and occlusion. Typical resolutions in the literature includeleveraging 2D or 3D ground truth for supervised learning, as well as imposinghand-crafted symmetry priors or using an implicit representation to hallucinatenovel viewpoints for unsupervised methods. In this work, we propose a generaladversarial learning framework for solving Unsupervised 2D to Explicit 3D StyleTransfer (UE3DST). Specifically, we merge two architectures: the unsupervisedexplicit 3D reconstruction network of Wu et al.\ and the Generative AdversarialNetwork (GAN) named StarGAN-v2. We experiment across three facial datasets(Basel Face Model, 3DFAW and CelebA-HQ) and show that our solution is able tooutperform well established solutions such as DepthNet in 3D reconstruction andPix2NeRF in conditional style transfer, while we also justify the individualcontributions of our model components via ablation. In contrast to theaforementioned baselines, our scheme produces features for explicit 3Drendering, which can be manipulated and utilized in downstream tasks.

Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image