We have tested the code on Ubuntu 18.04/20.04, Centos 7 and Windows 10.
Please decide whether to fetch the release (Option 1) or the up-to-date (Option 2).
Directly download the full-packed released package from Github:
(1) ROMP v1.1 with all features.
(2) ROMP v1.0 with some basic features to process images/videos/webcam.
git clone -b master --single-branch https://github.com/Arthur151/ROMP
We have update the model_data.
Please download the essential data (model_data.zip, demo_data.zip) and pre-trained model (trained_models) from :
(Option 1) Github release: model_data.zip, demo_data.zip, trained_models_try.zip or trained_models.zip
(Option 2) Google drive.
Please note that trained_models_try.zip is enough for processing image/video/webcam, trained_models.zip is just to re-implemente all results in our paper.
After the first step, the layout of ROMP folder should be
ROMP
- configs
- model_data
- romp
- trained_models
- scripts
- docs
- demo
- active_configs
Please refer to this instrcutions to install simple_romp and preparing SMPL model files in our format.
Now, you are supposed to get the SMPL_NEUTRAL.pth
, smil_packed_info.pth
, and SMPLA_NEUTRAL.pth
in ~/.romp
.
To further generate SMPL_MALE
and SMPL_FEMALE
, please run
romp.pack_smpl_info -source_dir=/path/to/smpl_model_data --gender='female'
romp.pack_smpl_info -source_dir=/path/to/smpl_model_data --gender='male'
1. Install Pytorch.
Please choose one of the following 4 options to install Pytorch via conda or pip. Here, we support to install with Python 3.9, 3.8 or 3.7. We recommend installing via conda (Option 1-3) so that ROMP env is clean and will not affect other repo.
conda create -n ROMP python=3.9
conda activate ROMP
conda install -n ROMP pytorch==1.10.0 torchvision==0.11.1 cudatoolkit=10.2 -c pytorch
conda create -n ROMP python==3.8.8
conda activate ROMP
conda install -n ROMP pytorch==1.10.0 torchvision==0.11.1 cudatoolkit=10.2 -c pytorch
conda create -n ROMP python==3.7.6
conda activate ROMP
conda install -n ROMP pytorch==1.10.0 torchvision==0.11.1 cudatoolkit=10.2 -c pytorch
To directly install via pip, you need to install CUDA 10.2 first (For Ubuntu, runsudo apt-get install cuda-10-2
).
Then install pytorch via:
pip install torch==1.10.0+cu102 torchvision==0.11.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html
2. (Optional) Install Pytorch3d for rendering, otherwise please refer to this instruction to use pyrender via seting renderer: pyrender
.
Please note that 'pyrender' can be only used on desktop. To train ROMP or run it on server without visualization hardware, please install 'pytorch3d' and set renderer: pytorch3d
in configs.
On Linux, please install via
# if you use Python3.9 (Option 1 or Option 4 with python3.9), please install pytorch3d via
pip install https://github.com/Arthur151/ROMP/releases/download/v1.1/pytorch3d-0.6.1-cp39-cp39-linux_x86_64.whl
# if you use Python3.8 (Option 2 or Option 4 with python3.8), please install pytorch3d via
pip install https://github.com/Arthur151/ROMP/releases/download/v1.1/pytorch3d-0.6.1-cp38-cp38-linux_x86_64.whl
# if you use Python3.7 (Option 3 or Option 4 with python3.7), please install pytorch3d via
pip install https://github.com/Arthur151/ROMP/releases/download/v1.1/pytorch3d-0.6.1-cp37-cp37m-linux_x86_64.whl
On Mac or Windows, please follow the official instruction to install.
cd ROMP
pip install -r requirements.txt
To deal with bugs when installing on Windows. To build some package wheels, 'Visual Studio Build Tools' and 'Visual C++ build tools workload' are required.
To install them with the Chocolatey on Windows, please install in cmd as administrator instead of Powershell, meanwhile, please ensure your network can access to google.
Please install the Chocolatey first and then run choco install visualstudio2019buildtools visualstudio2019-workload-vctools
.