# Mocycle-GAN: Unpaired Video-to-Video Translation

arXiv: https://arxiv.org/abs/1908.09514 · HTML: https://mlsanity.com/paper/1908.09514/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 30 of 37)

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- [Taming Visually Guided Sound Generation](https://mlsanity.com/paper/2110.08791/) (2021)
- [Self-Supervised Learning for Videos: A Survey](https://mlsanity.com/paper/2207.00419/) (2022)
- [Control3D: Towards Controllable Text-to-3D Generation](https://mlsanity.com/paper/2311.05461/) (2023)
- [ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors](https://mlsanity.com/paper/2311.05463/) (2023)
- [A Style and Semantic Memory Mechanism for Domain Generalization](https://mlsanity.com/paper/2112.07517/) (2021)
- [Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models](https://mlsanity.com/paper/2409.07452/) (2024)
- [VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation](https://mlsanity.com/paper/2403.17001/) (2024)
- [Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data](https://mlsanity.com/paper/2103.17204/) (2021)
- [3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models](https://mlsanity.com/paper/2311.05464/) (2023)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://mlsanity.com/paper/2104.02687/) (2021)
- [Transferrable Contrastive Learning for Visual Domain Adaptation](https://mlsanity.com/paper/2112.07516/) (2021)
- [Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization](https://mlsanity.com/paper/2303.13232/) (2023)
- [I2V-GAN: Unpaired Infrared-to-Visible Video Translation](https://mlsanity.com/paper/2108.00913/) (2021)
- [DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation](https://mlsanity.com/paper/2409.07454/) (2024)
- [Smart Director: An Event-Driven Directing System for Live Broadcasting](https://mlsanity.com/paper/2201.04024/) (2022)
- [GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator](https://mlsanity.com/paper/2603.25053/) (2026)
- [Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow](https://mlsanity.com/paper/2201.05723/) (2022)
- [ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation](https://mlsanity.com/paper/2204.12367/) (2022)
- [VGFlow: Visibility guided Flow Network for Human Reposing](https://mlsanity.com/paper/2211.08540/) (2022)
- [Reviewing Intelligent Cinematography: AI research for camera-based video production](https://mlsanity.com/paper/2405.05039/) (2024)
- [Sketch Me A Video](https://mlsanity.com/paper/2110.04710/) (2021)
- [3D-Aware Multi-Class Image-to-Image Translation with NeRFs](https://mlsanity.com/paper/2303.15012/) (2023)
- [Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction](https://mlsanity.com/paper/2308.08011/) (2023)
- [Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low Data](https://mlsanity.com/paper/2204.02494/) (2022)
- [FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery](https://mlsanity.com/paper/2310.00106/) (2023)
- [Translation-based Video-to-Video Synthesis](https://mlsanity.com/paper/2404.04283/) (2024)
- [DreamJourney: Perpetual View Generation with Video Diffusion Models](https://mlsanity.com/paper/2506.17705/) (2025)
- [Xp-GAN: Unsupervised Multi-object Controllable Video Generation](https://mlsanity.com/paper/2111.10233/) (2021)

## Abstract (arXiv)

Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications. Nevertheless, the extension of such synthesis from image-to-image to video-to-video is not trivial especially when capturing spatio-temporal structures in videos. The difficulty originates from the aspect that not only the visual appearance in each frame but also motion between consecutive frames should be realistic and consistent across transformation. This motivates us to explore both appearance structure and temporal continuity in video synthesis. In this paper, we present a new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator. Technically, Mocycle-GAN capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames. Extensive experiments are conducted on video-to-labels and labels-to-video translation, and superior results are reported when comparing to state-of-the-art methods. More remarkably, we qualitatively demonstrate our Mocycle-GAN for both flower-to-flower and ambient condition transfer.