C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds

Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 7949-7958

Abstract


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 multimodal 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.

Related Material


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[bibtex]
@InProceedings{Pumarola_2020_CVPR,
author = {Pumarola, Albert and Popov, Stefan and Moreno-Noguer, Francesc and Ferrari, Vittorio},
title = {C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}