Neural Rigging for blender using RigNet
This is an updated README to run bRigNet on Windows 10 21H2, CUDA 11.7, PyTorch 1.13.1 using Blender 2.90.1
Blender is the open source 3D application from the Blender Foundation. RigNet is the Machine Learning prediction for articulated characters. It has a dual license, GPL3 for open source projects, commercial otherwise. It was presented in the following papers
@InProceedings{AnimSkelVolNet,
title={Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets},
author={Zhan Xu and Yang Zhou and Evangelos Kalogerakis and Karan Singh},
booktitle={2019 International Conference on 3D Vision (3DV)},
year={2019}
}
@article{RigNet,
title={RigNet: Neural Rigging for Articulated Characters},
author={Zhan Xu and Yang Zhou and Evangelos Kalogerakis and Chris Landreth and Karan Singh},
journal={ACM Trans. on Graphics},
year={2020},
volume={39}
}
bRigNet requires SciPy, PyTorch and torch-geometric, along with torch-scatter and torch-sparse.
Use Blender 2.90.1 as the plugin does not work on newer versions.
Download the repoistory as a zip file and install it from the blender addons window. The RigNet submodule also have to be downloaded and insterted into the addon folder.
At present, the CUDA toolkit from nVidia is required, it can be found at the manufacturer website.
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Jun__8_16:59:34_Pacific_Daylight_Time_2022
Cuda compilation tools, release 11.7, V11.7.99
Build cuda_11.7.r11.7/compiler.31442593_0
conda create -n brignet python=3.7
conda activate brignet
cd "%APPDATA%\Blender Foundation\Blender\2.90\scripts\addons\brignet-main"
virtualenv _additional_modules
cd _additional_modules
Scripts\activate
pip install torch==1.13.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.13.1+cu117.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.13.1+cu117.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.13.1+cu117.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.13.1+cu117.html
pip install torch-geometric==1.7.2
Enable bRigNet in the blender addons, the preferences will show up.
Set the Modules path properties to the RigNet environment from the previous step
RigNet requires a trained model. They have made theirs available at this address. The checkpoint folder can be copied to the RigNet subfolder. A different location can be set in the addon preferences.
the bRigNet tab will show up in the Viewport tools. Select a character mesh as target. Please make sure it doesn't exceed the 5K triangles. You can use the Decimator modifier to reduce the polycount on a copy of the mesh, and select a Collection of high res model on which to transfer the final weights
Rigs generated using RigNet from the command line can be loaded via the Load Skeleton panel. Please select the *.obj and *.txt file and press the button Load Rignet character
The blender addon doesn't cover training yet. If you want to train your own model, please follow the instructions from the RigNet project.
This blender implementation of RigNet and the author of this add-on are NOT associated with the University of Massachusetts Amherst.
This add-on has received a research grant from the Blender Foundation.
This addon is released under the GNU General Public License version 3 (GPLv3). The RigNet subfolder is licensed under the General Public License Version 3 (GPLv3), or under a Commercial License.