installation-windows.md

Windows installation tutorial

Another issue#16 shows the whole process to deploy ECON on Windows

Dependencies and Installation

• Use Anaconda
• NVIDIA GPU + CUDA
• Wget for Windows
• Create a new folder on your C drive and rename it "wget" and move the downloaded "wget.exe" over there.
• Add the path to your wget folder to your system environment variables at Environment Variables > System Variables Path > Edit environment variable

• Install Git for Windows 64-bit
• Visual Studio Community 2022 (Make sure to check all the boxes as shown in the image below)

Getting started

Start by cloning the repo:

git clone https://github.com/yuliangxiu/ECON.git
cd ECON

Environment

• Windows 10 / 11
• CUDA=11.3
• Python = 3.8
• PyTorch >= 1.12.1 (official Get Started)
• Cupy >= 11.3.0 (offcial Installation)
• PyTorch3D = 0.7.1 (official INSTALL.md, recommend install-from-local-clone)

# install required packages
cd ECON
conda env create -f environment-windows.yaml
conda activate econ

# install pytorch and cupy
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
pip install cupy-cuda11x
pip install git+https://github.com/facebookresearch/pytorch3d.git@v0.7.1

# install libmesh & libvoxelize
cd lib/common/libmesh
python setup.py build_ext --inplace
cd ../libvoxelize
python setup.py build_ext --inplace

Issue#69: Discussion of additional argument --compiler=msvc in python setup.py build_ext --inplace

Register at ICON's website

Required:

• SMPL: SMPL Model (Male, Female)
• SMPL-X: SMPL-X Model, used for training
• SMPLIFY: SMPL Model (Neutral)
• PIXIE: PIXIE SMPL-X estimator

⚠️ Click Register now on all dependencies, then you can download them all with ONE account.

Downloading required models and extra data (make sure to install git and wget for windows for this to work)

cd ECON
bash fetch_data.sh # requires username and password

Citation

👍 Please consider citing these awesome HPS approaches: PyMAF-X, PIXIE

@article{pymafx2022,
  title={PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images},
  author={Zhang, Hongwen and Tian, Yating and Zhang, Yuxiang and Li, Mengcheng and An, Liang and Sun, Zhenan and Liu, Yebin},
  journal={arXiv preprint arXiv:2207.06400},
  year={2022}
}


@inproceedings{PIXIE:2021,
  title={Collaborative Regression of Expressive Bodies using Moderation},
  author={Yao Feng and Vasileios Choutas and Timo Bolkart and Dimitrios Tzionas and Michael J. Black},
  booktitle={International Conference on 3D Vision (3DV)},
  year={2021}
}