mlsanityData to 7 Oct 2026

Multimodal Unsupervised Image-to-Image Translation

arXiv 1804.04732 ↗First posted 12 Apr 2018cs.CV cs.LG stat.MLcited by 795

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Cited by 72 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT

Key facts. Cited by 795 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2018.

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Citing papers per year: 2020: 1; 2021: 253; 2022: 173; 2023: 168; 2024: 102; 2025: 54; 2026: 44.

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  2. 2024Improved Distribution Matching Distillation for Fast Image Synthesiscited by 648
  3. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
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  5. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
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  11. 2023I2SB: Image-to-Image Schr"odinger Bridgecited by 214
  12. 2023Style Aligned Image Generation via Shared Attentioncited by 214
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  4. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Modelscited by 191
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  6. 2021Audio-Driven Emotional Video Portraitscited by 180
  7. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  8. 2022EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equationscited by 172
  9. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 154
  10. 2021UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Modelscited by 133
  11. 2021Hallucinated Neural Radiance Fields in the Wildcited by 130
  12. 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
  13. 2022MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generationcited by 125
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  15. 2021Image-to-Image Translation: Methods and Applicationscited by 116
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  20. 2021Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoderscited by 109
  21. 2024Measuring Style Similarity in Diffusion Modelscited by 106
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  24. 2021Model-Based Domain Generalizationcited by 103
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  26. 2021GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worldscited by 98
  27. 2023Unpaired Image-to-Image Translation via Neural Schr"odinger Bridgecited by 96
  28. 2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
  29. 2021Only a Matter of Style: Age Transformation Using a Style-Based Regression Modelcited by 92
  30. 2021GAN-Control: Explicitly Controllable GANscited by 91
  31. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
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  34. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  35. 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
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  37. 2024Marabou 2.0: A Versatile Formal Analyzer of Neural Networkscited by 81
  38. 2021PD-GAN: Probabilistic Diverse GAN for Image Inpaintingcited by 80

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