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

arXiv: https://arxiv.org/abs/1908.07683 · HTML: https://mlsanity.com/paper/1908.07683/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [Enhancing Photorealism Enhancement](https://mlsanity.com/paper/2105.04619/) (2021)
- [Per-Clip Video Object Segmentation](https://mlsanity.com/paper/2208.01924/) (2022)
- [LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.05570/) (2021)
- [Learning to Associate Every Segment for Video Panoptic Segmentation](https://mlsanity.com/paper/2106.09453/) (2021)
- [Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2110.04111/) (2021)
- [Video Demoireing with Relation-Based Temporal Consistency](https://mlsanity.com/paper/2204.02957/) (2022)
- [Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data](https://mlsanity.com/paper/2103.17204/) (2021)
- [Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2303.09779/) (2023)
- [Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow](https://mlsanity.com/paper/2201.05723/) (2022)
- [Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction](https://mlsanity.com/paper/2308.08011/) (2023)
- [Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency](https://mlsanity.com/paper/2204.00795/) (2022)
- [Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation](https://mlsanity.com/paper/2310.07149/) (2023)
- [Translation-based Video-to-Video Synthesis](https://mlsanity.com/paper/2404.04283/) (2024)
- [WAIT: Feature Warping for Animation to Illustration video Translation using GANs](https://mlsanity.com/paper/2310.04901/) (2023)
- [Toward Accurate and Temporally Consistent Video Restoration from Raw Data](https://mlsanity.com/paper/2312.16247/) (2023)
- [BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering](https://mlsanity.com/paper/2403.06243/) (2024)
- [Deeply Dual Supervised learning for melanoma recognition](https://mlsanity.com/paper/2508.01994/) (2025)
- [Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling](https://mlsanity.com/paper/2508.06805/) (2025)
- [DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation](https://mlsanity.com/paper/2508.06816/) (2025)
- [VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation](https://mlsanity.com/paper/2508.06819/) (2025)

## Abstract (arXiv)

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.