README.md

Learning Category-Specific Mesh Reconstruction from Image Collections

Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik

University of California, Berkeley

Project Page

Requirements

• Python 2.7
• PyTorch tested on version 0.3.0.post4

Installation

Setup virtualenv

virtualenv venv_cmr
source venv_cmr/bin/activate
pip install -U pip
deactivate
source venv_cmr/bin/activate
pip install -r requirements.txt

Install Neural Mesh Renderer and Perceptual loss

cd external;
bash install_external.sh

Demo

1. From the cmr directory, download the trained model:

wget https://people.eecs.berkeley.edu/~kanazawa/cachedir/cmr/model.tar.gz & tar -vzxf model.tar.gz

You should see cmr/cachedir/snapshots/bird_net/

1. Run the demo:

python -m cmr.demo --name bird_net --num_train_epoch 500 --img_path cmr/demo_data/img1.jpg
python -m cmr.demo --name bird_net --num_train_epoch 500 --img_path cmr/demo_data/birdie.jpg

Training

Please see doc/train.md

Citation

If you use this code for your research, please consider citing:

@article{cmrKanazawa18,
  title={Learning Category-Specific Mesh Reconstruction
  from Image Collections},
  author = {Angjoo Kanazawa and
  Shubham Tulsiani
  and Alexei A. Efros
  and Jitendra Malik},
  journal={arXiv preprint arXiv:1803.07549},
  year={2018}
}