# Rig Inversion by Training a Differentiable Rig Function

arXiv: https://arxiv.org/abs/2301.09567 · HTML: https://mlsanity.com/paper/2301.09567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Accurate Face Rig Approximation with Deep Differential Subspace Reconstruction](https://mlsanity.com/paper/2006.01746/) (2020)

## Built on by (most cited first, 2 of 2)

- [Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation](https://mlsanity.com/paper/2507.18352/) (2025)
- [High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model](https://mlsanity.com/paper/2302.04820/) (2023)

## Abstract (arXiv)

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.