# imaginaire

**Repository Path**: mirrors_NVlabs/imaginaire

## Basic Information

- **Project Name**: imaginaire
- **Description**: 英伟达开源的一个新的 PyTorch 库「Imaginaire」，共包含 9 种英伟达开发的图像及视频合成方法
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2020-08-18
- **Last Updated**: 2025-10-11

## Categories & Tags

**Categories**: image-processing

**Tags**: None

## README

<img src="imaginaire_logo.svg" alt="imaginaire_logo.svg" height="360"/>

# Imaginaire
### [Docs](http://deepimagination.cc/) | [License](LICENSE.md) | [Installation](INSTALL.md) | [Model Zoo](MODELZOO.md)

Imaginaire is a [pytorch](https://pytorch.org/) library that contains
optimized implementation of several image and video synthesis methods developed at [NVIDIA](https://www.nvidia.com/en-us/).

## License

Imaginaire is released under [NVIDIA Software license](LICENSE.md).
For commercial use, please consult [NVIDIA Research Inquiries](https://www.nvidia.com/en-us/research/inquiries/).

## What's inside?

[![IMAGE ALT TEXT](http://img.youtube.com/vi/jgTX5OnAsYQ/0.jpg)](http://www.youtube.com/watch?v=jgTX5OnAsYQ "Imaginaire")

We have a tutorial for each model. Click on the model name, and your browser should take you to the tutorial page for the project.

### Supervised Image-to-Image Translation

|Algorithm Name                               | Feature                                                                                                         | Publication                                                   |
|:--------------------------------------------|:----------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
|[pix2pixHD](projects/pix2pixhd/README.md)     | Learn a mapping that converts a semantic image to a high-resolution photorealistic image.                       |    [Wang et. al. CVPR 2018](https://arxiv.org/abs/1711.11585) |
|[SPADE](projects/spade/README.md)             | Improve pix2pixHD on handling diverse input labels and delivering better output quality.                        |    [Park et. al. CVPR 2019](https://arxiv.org/abs/1903.07291) |


### Unsupervised Image-to-Image Translation


|Algorithm Name                               | Feature                                                                                                         | Publication                                                   |
|:--------------------------------------------|:----------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
|[UNIT](projects/unit/README.md)               | Learn a one-to-one mapping between two visual domains.                                                          |    [Liu et. al. NeurIPS 2017](https://arxiv.org/abs/1703.00848) |
|[MUNIT](projects/munit/README.md)             | Learn a many-to-many mapping between two visual domains.                                                        |    [Huang et. al. ECCV 2018](https://arxiv.org/abs/1804.04732) |
|[FUNIT](projects/funit/README.md)             | Learn a style-guided image translation model that can generate translations in unseen domains.                  |    [Liu et. al. ICCV 2019](https://arxiv.org/abs/1905.01723) |
|[COCO-FUNIT](projects/coco_funit/README.md)   | Improve FUNIT with a content-conditioned style encoding scheme for style code computation.                      |    [Saito et. al. ECCV 2020](https://arxiv.org/abs/2007.07431) |


### Video-to-video Translation


|Algorithm Name                               | Feature                                                                                                         | Publication                                                   |
|:--------------------------------------------|:----------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
|[vid2vid](projects/vid2vid/README.md)         | Learn a mapping that converts a semantic video to a photorealistic video.                                       |    [Wang et. al. NeurIPS 2018](https://arxiv.org/abs/1808.06601) |
|[fs-vid2vid](projects/fs_vid2vid/README.md)   | Learn a subject-agnostic mapping that converts a semantic video and an example image to a photoreslitic video.  |    [Wang et. al. NeurIPS 2019](https://arxiv.org/abs/1808.06601) |


### World-to-world Translation


|Algorithm Name                               | Feature                                                                                                         | Publication                                                   |
|:--------------------------------------------|:----------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
|[wc-vid2vid](projects/wc_vid2vid/README.md)   | Improve vid2vid on view consistency and long-term consistency.                                                  |    [Mallya et. al. ECCV 2020](https://arxiv.org/abs/2007.08509) |
|[GANcraft](projects/gancraft/README.md)   | Convert semantic block worlds to realistic-looking worlds.                                                  |    [Hao et. al. ICCV 2021](https://arxiv.org/abs/2104.07659) |