Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation

TL;DR

This paper introduces a novel framework for unpaired video-to-video translation that maintains semantic and temporal consistency, improving visual quality and stability without requiring paired training data.

Contribution

The proposed method incorporates content preserving and temporal consistency losses, addressing semantic and flickering artifacts in unpaired video translation tasks.

Findings

Outperforms previous methods in qualitative and quantitative evaluations

Reduces semantic label flipping and temporal flickering artifacts

Effective in domain adaptation scenarios

Abstract

In this paper, we investigate the problem of unpaired video-to-video translation. Given a video in the source domain, we aim to learn the conditional distribution of the corresponding video in the target domain, without seeing any pairs of corresponding videos. While significant progress has been made in the unpaired translation of images, directly applying these methods to an input video leads to low visual quality due to the additional time dimension. In particular, previous methods suffer from semantic inconsistency (i.e., semantic label flipping) and temporal flickering artifacts. To alleviate these issues, we propose a new framework that is composed of carefully-designed generators and discriminators, coupled with two core objective functions: 1) content preserving loss and 2) temporal consistency loss. Extensive qualitative and quantitative evaluations demonstrate the superior performance of the proposed method against previous approaches. We further apply our framework to a domain adaptation task and achieve favorable results.