  Inferring 3D object structures from a single image is an ill-posed task due
to depth ambiguity and occlusion. Typical resolutions in the literature include
leveraging 2D or 3D ground truth for supervised learning, as well as imposing
hand-crafted symmetry priors or using an implicit representation to hallucinate
novel viewpoints for unsupervised methods. In this work, we propose a general
adversarial learning framework for solving Unsupervised 2D to Explicit 3D Style
Transfer (UE3DST). Specifically, we merge two architectures: the unsupervised
explicit 3D reconstruction network of Wu et al.\ and the Generative Adversarial
Network (GAN) named StarGAN-v2. We experiment across three facial datasets
(Basel Face Model, 3DFAW and CelebA-HQ) and show that our solution is able to
outperform well established solutions such as DepthNet in 3D reconstruction and
Pix2NeRF in conditional style transfer, while we also justify the individual
contributions of our model components via ablation. In contrast to the
aforementioned baselines, our scheme produces features for explicit 3D
rendering, which can be manipulated and utilized in downstream tasks.
