# Dynamic Facial Asset and Rig Generation from a Single Scan

arXiv: https://arxiv.org/abs/2010.00560 · HTML: https://mlsanity.com/paper/2010.00560/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Relightable Gaussian Codec Avatars](https://mlsanity.com/paper/2312.03704/) (2023)
- [Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance](https://mlsanity.com/paper/2401.15687/) (2024)
- [ScanTalk: 3D Talking Heads from Unregistered Scans](https://mlsanity.com/paper/2403.10942/) (2024)
- [High-Res Facial Appearance Capture from Polarized Smartphone Images](https://mlsanity.com/paper/2212.01160/) (2022)
- [VRMM: A Volumetric Relightable Morphable Head Model](https://mlsanity.com/paper/2402.04101/) (2024)
- [Video-driven Neural Physically-based Facial Asset for Production](https://mlsanity.com/paper/2202.05592/) (2022)
- [Neural Parameterization for Dynamic Human Head Editing](https://mlsanity.com/paper/2207.00210/) (2022)
- [Learning Audio-Driven Viseme Dynamics for 3D Face Animation](https://mlsanity.com/paper/2301.06059/) (2023)
- [Relightable Neural Human Assets from Multi-view Gradient Illuminations](https://mlsanity.com/paper/2212.07648/) (2022)
- [High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse Rendering](https://mlsanity.com/paper/2401.08398/) (2024)
- [Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads](https://mlsanity.com/paper/2410.11041/) (2024)
- [Towards High-fidelity 3D Talking Avatar with Personalized Dynamic Texture](https://mlsanity.com/paper/2503.00495/) (2025)
- [Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation](https://mlsanity.com/paper/2207.09019/) (2022)
- [TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images](https://mlsanity.com/paper/2303.13743/) (2023)
- [RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data](https://mlsanity.com/paper/2511.18601/) (2025)
- [WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering](https://mlsanity.com/paper/2512.11237/) (2025)
- [OmniFaceRig: Fully Automatic Inner-Mouth-Aware Face Rigging Across Diverse 3D Character Topologies](https://mlsanity.com/paper/2606.08043/) (2026)
- [Topologically Consistent Multi-View Face Inference Using Volumetric Sampling](https://mlsanity.com/paper/2110.02948/) (2021)
- [Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generation](https://mlsanity.com/paper/2504.15259/) (2025)
- [FreeTalk: Emotional Topology-Free 3D Talking Heads](https://mlsanity.com/paper/2603.15512/) (2026)
- [FaceSnap: Real-Time Personalized Lightstage Facial Performance Capture](https://mlsanity.com/paper/2608.31033/) (2026)

## Abstract (arXiv)

The creation of high-fidelity computer-generated (CG) characters used in film and gaming requires intensive manual labor and a comprehensive set of facial assets to be captured with complex hardware, resulting in high cost and long production cycles. In order to simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial assets, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic and physically-based textures, as well as secondary facial components (e.g., teeth and eyeballs). Built upon a facial database consisting of pore-level details, with over 4,000 scans of varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with cutting-edge industry pipelines for facial animation and rendering. We demonstrate that our framework is robust and effective by inferring on a wide range of novel subjects, and illustrate compelling rendering results while animating faces with generated customized physically-based dynamic textures.