  We present a new weakly supervised learning-based method for generating novel
category-specific 3D shapes from unoccluded image collections. Our method is
weakly supervised and only requires silhouette annotations from unoccluded,
category-specific objects. Our method does not require access to the object's
3D shape, multiple observations per object from different views, intra-image
pixel-correspondences, or any view annotations. Key to our method is a novel
multi-projection generative adversarial network (MP-GAN) that trains a 3D shape
generator to be consistent with multiple 2D projections of the 3D shapes, and
without direct access to these 3D shapes. This is achieved through multiple
discriminators that encode the distribution of 2D projections of the 3D shapes
seen from a different views. Additionally, to determine the view information
for each silhouette image, we also train a view prediction network on
visualizations of 3D shapes synthesized by the generator. We iteratively
alternate between training the generator and training the view prediction
network. We validate our multi-projection GAN on both synthetic and real image
datasets. Furthermore, we also show that multi-projection GANs can aid in
learning other high-dimensional distributions from lower dimensional training
datasets, such as material-class specific spatially varying reflectance
properties from images.

Computer Vision and Pattern Recognition;Graphics