We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modelling of video. To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions. More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously. Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally as in the case of pixel-level autoregressive models, or do not directly optimize the likelihood of the data. In this paper, we propose the hierarchical conditional flow (HCFlow) as a unified framework for image SR and image rescaling.

However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures. 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.Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. 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. 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. 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. Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds. 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. Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. Use an activity diagram to describe the internal behavior of a method and represent a flow driven by internally generated actions. You can do similar thing with AutoEncoder+GAN. If you are intereseted in feature-subtraction with Conditional-Generative model, take a look at AE-GAN. VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.

Download a PDF of the paper titled C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds, by Albert Pumarola and 3 other authors Download PDF 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. We find that Conditional Normalizing Flow can work as feature subtraction for given data.

Modelio conditional flow