  We propose a novel generative adversarial network (GAN) for the task of
unsupervised learning of 3D representations from natural images. Most
generative models rely on 2D kernels to generate images and make few
assumptions about the 3D world. These models therefore tend to create blurry
images or artefacts in tasks that require a strong 3D understanding, such as
novel-view synthesis. HoloGAN instead learns a 3D representation of the world,
and to render this representation in a realistic manner. Unlike other GANs,
HoloGAN provides explicit control over the pose of generated objects through
rigid-body transformations of the learnt 3D features. Our experiments show that
using explicit 3D features enables HoloGAN to disentangle 3D pose and identity,
which is further decomposed into shape and appearance, while still being able
to generate images with similar or higher visual quality than other generative
models. HoloGAN can be trained end-to-end from unlabelled 2D images only.
Particularly, we do not require pose labels, 3D shapes, or multiple views of
the same objects. This shows that HoloGAN is the first generative model that
learns 3D representations from natural images in an entirely unsupervised
manner.
