Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient

TL;DR

This paper introduces RL-V2V-GAN, a reinforcement learning-based method for unsupervised, few-shot video-to-video synthesis that preserves style and achieves temporal coherence without needing paired data.

Contribution

It presents a novel reinforcement learning framework with policy gradient and ConvLSTM layers for unpaired, few-shot video synthesis, advancing the state of the art.

Findings

Produces temporally coherent videos

Does not require paired training data

Effective in few-shot learning scenarios

Abstract

The development of sophisticated models for video-to-video synthesis has been facilitated by recent advances in deep reinforcement learning and generative adversarial networks (GANs). In this paper, we propose RL-V2V-GAN, a new deep neural network approach based on reinforcement learning for unsupervised conditional video-to-video synthesis. While preserving the unique style of the source video domain, our approach aims to learn a mapping from a source video domain to a target video domain. We train the model using policy gradient and employ ConvLSTM layers to capture the spatial and temporal information by designing a fine-grained GAN architecture and incorporating spatio-temporal adversarial goals. The adversarial losses aid in content translation while preserving style. Unlike traditional video-to-video synthesis methods requiring paired inputs, our proposed approach is more general because it does not require paired inputs. Thus, when dealing with limited videos in the target domain, i.e., few-shot learning, it is particularly effective. Our experiments show that RL-V2V-GAN can produce temporally coherent video results. These results highlight the potential of our approach for further advances in video-to-video synthesis.