  We present a technique to synthesize and analyze volume-rendered images using
generative models. We use the Generative Adversarial Network (GAN) framework to
compute a model from a large collection of volume renderings, conditioned on
(1) viewpoint and (2) transfer functions for opacity and color. Our approach
facilitates tasks for volume analysis that are challenging to achieve using
existing rendering techniques such as ray casting or texture-based methods. We
show how to guide the user in transfer function editing by quantifying expected
change in the output image. Additionally, the generative model transforms
transfer functions into a view-invariant latent space specifically designed to
synthesize volume-rendered images. We use this space directly for rendering,
enabling the user to explore the space of volume-rendered images. As our model
is independent of the choice of volume rendering process, we show how to
analyze volume-rendered images produced by direct and global illumination
lighting, for a variety of volume datasets.
