Learning Category-Specific Mesh Reconstruction from Image Collections
Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik
University of California, Berkeley In ECCV, 2018
This code is no longer actively maintained. For pytorch 1.x, python3, and pytorch NMR support, please see this implementation from chenyuntc.
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
- From the
cmrdirectory, 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/
- 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:
@inProceedings{cmrKanazawa18,
title={Learning Category-Specific Mesh Reconstruction
from Image Collections},
author = {Angjoo Kanazawa and
Shubham Tulsiani
and Alexei A. Efros
and Jitendra Malik},
booktitle={ECCV},
year={2018}
}