  Flow-based generative models have highly desirable properties like exact
log-likelihood evaluation and exact latent-variable inference, however they are
still in their infancy and have not received as much attention as alternative
generative models. In this paper, we introduce C-Flow, a novel conditioning
scheme that brings normalizing flows to an entirely new scenario with great
possibilities for multi-modal data modeling. C-Flow is based on a parallel
sequence of invertible mappings in which a source flow guides the target flow
at every step, enabling fine-grained control over the generation process. We
also devise a new strategy to model unordered 3D point clouds that, in
combination with the conditioning scheme, makes it possible to address 3D
reconstruction from a single image and its inverse problem of rendering an
image given a point cloud. We demonstrate our conditioning method to be very
adaptable, being also applicable to image manipulation, style transfer and
multi-modal image-to-image mapping in a diversity of domains, including RGB
images, segmentation maps, and edge masks.
