Multimodal Unsupervised Image-To-Image Translation

14 papers with code • 6 benchmarks • 4 datasets

Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.

( Image credit: MUNIT: Multimodal UNsupervised Image-to-image Translation )


Use these libraries to find Multimodal Unsupervised Image-To-Image Translation models and implementations

Most implemented papers

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

junyanz/pytorch-CycleGAN-and-pix2pix ICCV 2017

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.

StarGAN v2: Diverse Image Synthesis for Multiple Domains

clovaai/stargan-v2 CVPR 2020

A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.

Multimodal Unsupervised Image-to-Image Translation

nvlabs/MUNIT ECCV 2018

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.

Unsupervised Image-to-Image Translation Networks

mingyuliutw/UNIT NeurIPS 2017

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.

Diverse Image-to-Image Translation via Disentangled Representations

HsinYingLee/DRIT ECCV 2018

Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

HelenMao/MSGAN CVPR 2019

In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.

Lifespan Age Transformation Synthesis

royorel/Lifespan_Age_Transformation_Synthesis ECCV 2020

Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process.

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

PramuPerera/In2I 26 Nov 2017

In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images.

Breaking the cycle -- Colleagues are all you need

Onr/Council-GAN 24 Nov 2019

(2) Since it does not need to support the cycle constraint, no irrelevant traces of the input are left on the generated image.

High-Resolution Daytime Translation Without Domain Labels

saic-mdal/HiDT CVPR 2020

We present the high-resolution daytime translation (HiDT) model for this task.