# Improving Facial Rig Semantics for Tracking and Retargeting

arXiv: https://arxiv.org/abs/2508.08429 · HTML: https://mlsanity.com/paper/2508.08429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 103 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 30 of 41)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [I M Avatar: Implicit Morphable Head Avatars from Videos](https://mlsanity.com/paper/2112.07471/) (2021)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [Deep Appearance Models for Face Rendering](https://mlsanity.com/paper/1808.00362/) (2018)
- [MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction](https://mlsanity.com/paper/1703.10580/) (2017)
- [Pixel Codec Avatars](https://mlsanity.com/paper/2104.04638/) (2021)
- [Real-Time Radiance Fields for Single-Image Portrait View Synthesis](https://mlsanity.com/paper/2305.02310/) (2023)
- [Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing](https://mlsanity.com/paper/2103.15432/) (2021)
- [FLARE: Fast Learning of Animatable and Relightable Mesh Avatars](https://mlsanity.com/paper/2310.17519/) (2023)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis](https://mlsanity.com/paper/2309.16859/) (2023)
- [Practical Face Reconstruction via Differentiable Ray Tracing](https://mlsanity.com/paper/2101.05356/) (2021)
- [Towards Practical Capture of High-Fidelity Relightable Avatars](https://mlsanity.com/paper/2309.04247/) (2023)
- [Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures](https://mlsanity.com/paper/2410.00630/) (2024)
- [Photorealistic Facial Texture Inference Using Deep Neural Networks](https://mlsanity.com/paper/1612.00523/) (2016)
- [Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild](https://mlsanity.com/paper/2305.08296/) (2023)
- [BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis](https://mlsanity.com/paper/2311.05521/) (2023)
- [3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow](https://mlsanity.com/paper/2404.09819/) (2024)
- [VRMM: A Volumetric Relightable Morphable Head Model](https://mlsanity.com/paper/2402.04101/) (2024)
- [VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence](https://mlsanity.com/paper/2405.16204/) (2024)
- [MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading](https://mlsanity.com/paper/2312.13091/) (2023)
- [S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image](https://mlsanity.com/paper/2203.07732/) (2022)
- [GANHead: Towards Generative Animatable Neural Head Avatars](https://mlsanity.com/paper/2304.03950/) (2023)
- [Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.06536/) (2016)
- [Facial Expression Retargeting from Human to Avatar Made Easy](https://mlsanity.com/paper/2008.05110/) (2020)
- [UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures](https://mlsanity.com/paper/2401.11078/) (2024)

## Abstract (arXiv)

In this paper, we consider retargeting a tracked facial performance to either another person or to a virtual character in a game or virtual reality (VR) environment. We remove the difficulties associated with identifying and retargeting the semantics of one rig framework to another by utilizing the same framework (3DMM, FLAME, MetaHuman, etc.) for both subjects. Although this does not constrain the choice of framework when retargeting from one person to another, it does force the tracker to use the game/VR character rig when retargeting to a game/VR character. We utilize volumetric morphing in order to fit facial rigs to both performers and targets; in addition, a carefully chosen set of Simon-Says expressions is used to calibrate each rig to the motion signatures of the relevant performer or target. Although a uniform set of Simon-Says expressions can likely be used for all person to person retargeting, we argue that person to game/VR character retargeting benefits from Simon-Says expressions that capture the distinct motion signature of the game/VR character rig. The Simon-Says calibrated rigs tend to produce the desired expressions when exercising animation controls (as expected). Unfortunately, these well-calibrated rigs still lead to undesirable controls when tracking a performance (a well-behaved function can have an arbitrarily ill-conditioned inverse), even though they typically produce acceptable geometry reconstructions. Thus, we propose a fine-tuning approach that modifies the rig used by the tracker in order to promote the output of more semantically meaningful animation controls, facilitating high efficacy retargeting. In order to better address real-world scenarios, the fine-tuning relies on implicit differentiation so that the tracker can be treated as a (potentially non-differentiable) black box.