KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #deepseek-harness #typescript #ai-agent #codex #developer-tools #claude-code #windows #mcp #python #desktop-app

rig-inversion

★ 95 Open GitHub ↗

Example code for the technique described in the paper "Rig Inversion by Training a Differentiable Rig Function" published at SIGGRAPH Asia 2022.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Rig Inversion by Training a Differentiable Rig Function

This code serves an an example of using the technique described in the paper Rig Inversion by Training a Differentiable Rig Function published at Siggraph Asia 2022.

[Rig Inversion by Training a Differentiable Rig Function](https://arxiv.org/abs/2301.09567)

Rig inversion is demonstrated using a toy rig.

## How to use

### Step 1 : Generate a training dataset using your rig

python generate_toy_dataset.py

### Step 2 : Train a model to approximate the rig and test it using animations

python train_rig_approximation.py

### Step 3 : Inverse the rig using the rig approximation trained in step 2

python inverse_rig.py

## Contents

- `generate_toy_dataset.py`: Will generate a dataset to train the rig approximation of our toy rig
- `inverse_rig.py`: Inverse the rig for the test mesh data using a trained rig approximation
- `model.py`: Model definition for rig approximation and rig inversion
- `rig.py`: Definition of our toy rig function
- `train_rig_approximation.py`: Trains a rig approximation using a dataset of rig function data points

## References

If you use this technique, please cite the paper:

> *Marquis Bolduc, Mathieu and Phan, Hau Nghiep*. **[Rig Inversion by Training a Differentiable Rig Function](https://arxiv.org/abs/2301.09567)**. SIGGRAPH Asia 2022 Technical Communications.

BibTeX:

```
@inproceedings{10.1145/3550340.3564218,
author = {Marquis Bolduc, Mathieu and Phan, Hau Nghiep},
title = {Rig Inversion by Training a Differentiable Rig Function},
year = {2022},
isbn = {9781450394659},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3550340.3564218},
doi = {10.1145/3550340.3564218},
abstract = {Rig inversion is the problem of creating a method that can find the rig parameter vector that best approximates a given input mesh. In this paper we propose to solve this problem by first obtaining a differentiable rig function by training a multi layer perceptron to approximate the rig function. This differentiable rig function can then be used to train a deep learning model of rig inversion.},
booktitle = {SIGGRAPH Asia 2022 Technical Communications},
articleno = {15},
numpages = {4},
keywords = {computer animation, neural networks, rig inversion},
location = {Daegu, Republic of Korea},
series = {SA '22}
}
```

## Authors

<a href="https://seed.ea.com"><img src="logo/SEED.jpg" width="150px"></a> 
Search for Extraordinary Experiences Division (SEED) - Electronic Arts http://seed.ea.com 
We are a cross-disciplinary team within EA Worldwide Studios. 
Our mission is to explore, build and help define the future of interactive entertainment.

This technique was created by Mathieu Marquis Bolduc and Hau Nghiep Phan

## Licenses

- The source code uses *BSD 3-Clause License* as detailed in [LICENSE.md](LICENSE.md)

rig-inversion

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README