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Automatic rigging using neural network from RigNet
| Date | Stars |
|---|---|
| 2026-07-31 | 446 |
| 2026-08-05 | 446 |
| 2026-08-06 | 446 |
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bRigNet
---------
Neural Rigging for [blender](https://www.blender.org/ "Blender Home Page") using [RigNet](https://zhan-xu.github.io/rig-net/ "RigNet Home Page")
**THIS ADD-ON IS DEAD AND WILL HADLY WORK, LET'S DISCUSS ABOUT BRINGING IT BACK IN THE ISSUES AND DISCUSSIONS PAGES**
-------------------------------------------------------------------------------------------------------------------
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}
}
```
## Setup
bRigNet requires SciPy, PyTorch and torch-geometric, along with torch-scatter and torch-sparse.
## Installation
Download the Neural Rigging add-on as a .zip file and install it from the blender addons window,
or copy the code to the blender scripts path
### Install dependencies via "Install" button
At present, the CUDA toolkit from nVidia is required, it can be found at the
[manufacturer website](https://developer.nvidia.com)
A dependency installer is available in the preferences.
* Install CUDA. At present prebuilt packages support versions 10.1, 10.2, 11.1
* In the addon preferences, make sure that the Cuda version is detected correctly.
* Hit the "Install" button. It can take time!
Alternatively, Environment managers, like conda or virtualenv can be used to ease the install.
### Install dependencies using *conda*
Anaconda is a data science platform from Anaconda Inc., it can be downloaded from the
[company website](https://www.anaconda.com/).
A lightweight version called [Miniconda](https://docs.conda.io/en/latest/miniconda.html) is available.
Both versions include the package manager 'conda' used in the following steps.
- Open a Miniconda or Anaconda prompt
- Create a Conda Environment and activate it
```
conda create -n brignet python=3.7
conda activate brignet_deps
```
- Install PyTorch. If CUDA is installed, the CUDA version can be queried in a command prompt. For example
```
nvcc --version
```
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:32:27_Pacific_Daylight_Time_2019
Cuda compilation tools, release 10.2, V10.2.89
```
In this case PyTorch can be installed in the command prompt via
```
conda install pytorch==1.8.1 cudatoolkit=10.2 -c pytorch
```
More complete information on the PyTorch command line can be found at the [PyTorch website](https://pytorch.org/)
The install command on non-cuda devices is
```
conda install pytorch==1.8.1 cpuonly -c pytorch
```
- Install torch utilities. The syntax follows the pattern
```
pip install [package-name] -f https://pytorch-geometric.com/whl/torch-[version]+cu[cuda-version].html
```
```
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.1+cu102.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.1+cu102.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.1+cu102.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.1+cu102.html
pip install torch-geometric
```
Alternatively, pip can try and build the libraries. Even if part of torch-sparse fails without a proper environment,
the relevant modules are usually built
```
pip install torch-scatter
pip install torch-sparse
pip install torch-geometrExcerpt of 7,366 characters
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