# CoMoGAN: continuous model-guided image-to-image translation

arXiv: https://arxiv.org/abs/2103.06879 · HTML: https://mlsanity.com/paper/2103.06879/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 77 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 30 of 63)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [SEAN: Image Synthesis with Semantic Region-Adaptive Normalization](https://mlsanity.com/paper/1911.12861/) (2019)
- [On the "steerability" of generative adversarial networks](https://mlsanity.com/paper/1907.07171/) (2019)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [GANalyze: Toward Visual Definitions of Cognitive Image Properties](https://mlsanity.com/paper/1906.10112/) (2019)
- [DLOW: Domain Flow for Adaptation and Generalization](https://mlsanity.com/paper/1812.05418/) (2018)
- [Image to Image Translation for Domain Adaptation](https://mlsanity.com/paper/1712.00479/) (2017)

## Built on by (most cited first, 27 of 27)

- [Self-supervised Monocular Depth Estimation: Let's Talk About The Weather](https://mlsanity.com/paper/2307.08357/) (2023)
- [NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night](https://mlsanity.com/paper/2204.05538/) (2022)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [ManiFest: Manifold Deformation for Few-shot Image Translation](https://mlsanity.com/paper/2111.13681/) (2021)
- [Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information](https://mlsanity.com/paper/2112.05758/) (2021)
- [GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling](https://mlsanity.com/paper/2210.14145/) (2022)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Seeing Objects in dark with Continual Contrastive Learning](https://mlsanity.com/paper/2112.02891/) (2021)
- [Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition](https://mlsanity.com/paper/2203.09771/) (2022)
- [Contrastive Monotonic Pixel-Level Modulation](https://mlsanity.com/paper/2207.11517/) (2022)
- [Domain-Scalable Unpaired Image Translation via Latent Space Anchoring](https://mlsanity.com/paper/2306.14879/) (2023)
- [Learning to Generate Training Datasets for Robust Semantic Segmentation](https://mlsanity.com/paper/2308.02535/) (2023)
- [Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2309.13188/) (2023)
- [Leveraging Local Domains for Image-to-Image Translation](https://mlsanity.com/paper/2109.04468/) (2021)
- [ChildPredictor: A Child Face Prediction Framework with Disentangled Learning](https://mlsanity.com/paper/2204.09962/) (2022)
- [Do Deep Neural Networks Always Perform Better When Eating More Data?](https://mlsanity.com/paper/2205.15187/) (2022)
- [A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective](https://mlsanity.com/paper/2211.10963/) (2022)
- [Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection](https://mlsanity.com/paper/2309.06747/) (2023)
- [Domain Adaptation with a Single Vision-Language Embedding](https://mlsanity.com/paper/2410.21361/) (2024)
- [TimeNeRF: Building Generalizable Neural Radiance Fields across Time from Few-Shot Input Views](https://mlsanity.com/paper/2507.13929/) (2025)
- [Adapting Depth Anything to Adverse Imaging Conditions with Events](https://mlsanity.com/paper/2601.02020/) (2026)
- [Image-to-Image Translation Framework Embedded with Rotation Symmetry Priors](https://mlsanity.com/paper/2604.12805/) (2026)
- [Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks](https://mlsanity.com/paper/2204.01795/) (2022)
- [Spectrum-inspired Low-light Image Translation for Saliency Detection](https://mlsanity.com/paper/2303.10145/) (2023)
- [RoNet: Rotation-oriented Continuous Image Translation](https://mlsanity.com/paper/2404.04474/) (2024)
- [Generative Face Parsing Map Guided 3D Face Reconstruction Under Occluded Scenes](https://mlsanity.com/paper/2412.18920/) (2024)
- [When To Adapt? Adapting the Model or Data in Federated Medical Imaging](https://mlsanity.com/paper/2605.00892/) (2026)

## Abstract (arXiv)

CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature. Our code is available at http://github.com/cv-rits/CoMoGAN .