wpeebles/gangealing

Health v2 · maintenance only

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1012 stars · 121 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of GANgealing, a CVPR 2022 method that trains a Spatial Transformer to densely align images using GAN-generated training data. It includes pre-trained models, training/evaluation code, and visualization tools for dense visual alignment and correspondence on real images and videos.

Use cases

• learn dense visual correspondence without labeled data
• align unaligned image datasets to a common template
• propagate edits like cartoon eyes from a template image to real videos
• run mixed reality effects on cat videos with pre-trained spatial transformers
• train a spatial transformer on StyleGAN2 samples
• evaluate self-supervised correspondence algorithms
• warp images with anti-aliased grid sampling and CUDA splatting

When to choose

• you need self-supervised dense correspondence or alignment without annotation
• you want to propagate annotations or edits from a template to real images/videos
• you are reproducing or building on the GAN-Supervised Dense Visual Alignment paper
• you have GPU resources and want pre-trained models for eight datasets

When to avoid

• you need a production-ready, actively maintained computer vision library
• you lack a CUDA GPU since key components require it
• your task is generic object detection or segmentation rather than dense alignment
• you need correspondence for arbitrary object categories not covered by the pre-trained GANs

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python cross-platform pytorch gan stylegan2 dense-correspondence spatial-transformer visual-alignment tracking congealing research-code cvpr2022 gpu linux

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