Imaginaire - NVIDIA's Deep Imagination Team's PyTorch Library

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Overview

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Imaginaire

Docs | License | Installation | Model Zoo

Imaginaire is a pytorch library that contains optimized implementation of several image and video synthesis methods developed at NVIDIA.

License

Imaginaire is released under NVIDIA Software license. For commercial use, please consult NVIDIA Research Inquiries.

What's inside?

IMAGE ALT TEXT

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 Learn a mapping that converts a semantic image to a high-resolution photorealistic image. Wang et. al. CVPR 2018
SPADE Improve pix2pixHD on handling diverse input labels and delivering better output quality. Park et. al. CVPR 2019

Unsupervised Image-to-Image Translation

Algorithm Name Feature Publication
UNIT Learn a one-to-one mapping between two visual domains. Liu et. al. NeurIPS 2017
MUNIT Learn a many-to-many mapping between two visual domains. Huang et. al. ECCV 2018
FUNIT Learn a style-guided image translation model that can generate translations in unseen domains. Liu et. al. ICCV 2019
COCO-FUNIT Improve FUNIT with a content-conditioned style encoding scheme for style code computation. Saito et. al. ECCV 2020

Video-to-video Translation

Algorithm Name Feature Publication
vid2vid Learn a mapping that converts a semantic video to a photorealistic video. Wang et. al. NeurIPS 2018
fs-vid2vid Learn a subject-agnostic mapping that converts a semantic video and an example image to a photoreslitic video. Wang et. al. NeurIPS 2019

World-to-world Translation

Algorithm Name Feature Publication
wc-vid2vid Improve vid2vid on view consistency and long-term consistency. Mallya et. al. ECCV 2020
GANcraft Convert semantic block worlds to realistic-looking worlds. Hao et. al. ICCV 2021
Owner
NVIDIA Research Projects
NVIDIA Research Projects
GitHub Repository
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