  We present BlockGAN, an image generative model that learns object-aware 3D
scene representations directly from unlabelled 2D images. Current work on scene
representation learning either ignores scene background or treats the whole
scene as one object. Meanwhile, work that considers scene compositionality
treats scene objects only as image patches or 2D layers with alpha maps.
Inspired by the computer graphics pipeline, we design BlockGAN to learn to
first generate 3D features of background and foreground objects, then combine
them into 3D features for the wholes cene, and finally render them into
realistic images. This allows BlockGAN to reason over occlusion and interaction
between objects' appearance, such as shadow and lighting, and provides control
over each object's 3D pose and identity, while maintaining image realism.
BlockGAN is trained end-to-end, using only unlabelled single images, without
the need for 3D geometry, pose labels, object masks, or multiple views of the
same scene. Our experiments show that using explicit 3D features to represent
objects allows BlockGAN to learn disentangled representations both in terms of
objects (foreground and background) and their properties (pose and identity).
