Unsupervised Keypoints from Pretrained Diffusion Models

Eric Hedlin, Gopal Sharma, Shweta Mahajan, Xingzhe He, Hossam Isack, Abhishek Kar, Helge Rhodin, Andrea Tagliasacchi, Kwang Moo Yi

Project Page

For more detailed information, visit our project page or read our paper

Requirements

Set up environment

Create a conda environment using the provided requirements.yaml:

conda env create -f requirements.yaml
conda activate StableKeypoints

Download datasets

The CelebA, Taichi, Human3.6m, DeepFashion, and CUB datasets can be found on their websites.

Preprocessed data for CelebA, and CUB can be found in Autolink's repository.

Usage

To use the code, run:

python3 -m unsupervised_keypoints.main [arguments]

Main Arguments

• --dataset_loc: Path to the dataset.
• --dataset_name: Name of the dataset.
• --num_steps: Number of steps (default 500, up to 10,000 for non-human datasets).
• --evaluation_method: Following baselines, the evaluation method varies by dataset:
  • CelebA: 'inter_eye_distance'
  • CUB: 'visible'
  • Taichi: 'mean_average_error' (renormalized per keypoint)
  • DeepFashion: 'pck'
  • Human3.6M: 'orientation_invariant'
• --save_folder: Output save location (default "outputs" inside the repo).

Example Usage

python3 -m unsupervised_keypoints.main --dataset_loc /path/to/dataset --dataset_name celeba_wild --evaluation_method inter_eye_distance --save_folder /path/to/save

If you want to use a custom dataset you can run

python3 -m unsupervised_keypoints.main --dataset_loc /path/to/dataset --dataset_name custom

Precomputed tokens

We provide the precomputed tokens here

BibTeX

@article{hedlin2023keypoints,
  title={Unsupervised Keypoints from Pretrained Diffusion Models},
  author={Hedlin, Eric and Sharma, Gopal and Mahajan, Shweta and He, Xingzhe and Isack, Hossam and Rhodin, Abhishek Kar Helge and Tagliasacchi, Andrea and Yi, Kwang Moo},
  journal={arXiv preprint arXiv:2312.00065},
  year={2023}
}