Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa. CVPR, 2021 (Oral presentation).

We present KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. Our approach produces intuitive and semantically consistent control of shape deformations. Moreover, our discovered 3D keypoints are consistent across object category instances despite large shape variations. Since our method is unsupervised, it can be readily deployed to new object categories without requiring expensive annotations for 3D keypoints and deformations.

Clone the repo

git clone https://github.com/tomasjakab/keypoint_deformer
cd keypoint_deformer

Install using conda:

conda env create -f environment.yml 
conda activate keypointdeformer

Set-up python path:

export PYTHONPATH=$PYTHONPATH:$(pwd)

Download ShapeNet to data/shapenet. The path to ShapeNet can be also customized in config files configs/* with the option mesh_dir.

To train a model on the airplane category with 8 unsupervised keypoints run:

python scripts/main.py -c configs/airplane-8kpt.yaml

To train a model on the chair category with 12 unsupervised keypoints run:

python scripts/main.py -c configs/chair-12kpt.yaml

To test the trained model run:

python scripts/main.py -c configs/airplane-8kpt.yaml -t configs/test.yaml 

This will create result files in logs/airplane-8kpt/test/<SAMPLE NAME>. The file source_mesh.obj contains the input mesh and the file source_keypoints.txt predicted unsupervised keypoints.

To visualize the results run:

python browse3d/browse3d.py --log_dir logs/airplane-8kpt/test --port 5050

and open localhost:5050 in your web browser.

Try the interactive demo without any instalation.

Parts of the code are based on Neural Cages.