  Recent advancements in deep learning opened new opportunities for learning a
high-quality 3D model from a single 2D image given sufficient training on
large-scale data sets. However, the significant imbalance between available
amount of images and 3D models, and the limited availability of labeled 2D
image data (i.e. manually annotated pairs between images and their
corresponding 3D models), severely impacts the training of most supervised deep
learning methods in practice. In this paper, driven by a novel design of
adversarial networks, we have developed an unsupervised learning paradigm to
reconstruct 3D models from a single 2D image, which is free of manually
annotated pairwise input image and its associated 3D model. Particularly, the
paradigm begins with training an adaption network via autoencoder with
adversarial loss, which embeds unpaired 2D synthesized image domain with real
world image domain to a shared latent vector space. Then, we jointly train a 3D
deconvolutional network to transform the latent vector space to the 3D object
space together with the embedding process. Our experiments verify our network's
robust and superior performance in handling 3D volumetric object generation
from a single 2D image.
