# Learning Formation of Physically-Based Face Attributes

arXiv: https://arxiv.org/abs/2004.03458 · HTML: https://mlsanity.com/paper/2004.03458/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 30 of 94)

- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [TAVA: Template-free Animatable Volumetric Actors](https://mlsanity.com/paper/2206.08929/) (2022)
- [Relightable Gaussian Codec Avatars](https://mlsanity.com/paper/2312.03704/) (2023)
- [FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset](https://mlsanity.com/paper/2203.14057/) (2022)
- [Learning Neural Parametric Head Models](https://mlsanity.com/paper/2212.02761/) (2022)
- [ClipFace: Text-guided Editing of Textured 3D Morphable Models](https://mlsanity.com/paper/2212.01406/) (2022)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing](https://mlsanity.com/paper/2103.15432/) (2021)
- [URAvatar: Universal Relightable Gaussian Codec Avatars](https://mlsanity.com/paper/2410.24223/) (2024)
- [Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance](https://mlsanity.com/paper/2401.15687/) (2024)
- [FitMe: Deep Photorealistic 3D Morphable Model Avatars](https://mlsanity.com/paper/2305.09641/) (2023)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field](https://mlsanity.com/paper/2309.17128/) (2023)
- [Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement](https://mlsanity.com/paper/2106.11423/) (2021)
- [Practical Face Reconstruction via Differentiable Ray Tracing](https://mlsanity.com/paper/2101.05356/) (2021)
- [Relightify: Relightable 3D Faces from a Single Image via Diffusion Models](https://mlsanity.com/paper/2305.06077/) (2023)
- [Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation](https://mlsanity.com/paper/2205.03962/) (2022)
- [Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild](https://mlsanity.com/paper/2305.08296/) (2023)
- [HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details](https://mlsanity.com/paper/2303.11225/) (2023)
- [SUPR: A Sparse Unified Part-Based Human Representation](https://mlsanity.com/paper/2210.13861/) (2022)
- [Generalizable One-shot Neural Head Avatar](https://mlsanity.com/paper/2306.08768/) (2023)
- [Learning a 3D Morphable Face Reflectance Model from Low-cost Data](https://mlsanity.com/paper/2303.11686/) (2023)
- [VRMM: A Volumetric Relightable Morphable Head Model](https://mlsanity.com/paper/2402.04101/) (2024)
- [RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset](https://mlsanity.com/paper/2303.12564/) (2023)
- [MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading](https://mlsanity.com/paper/2312.13091/) (2023)
- [Video-driven Neural Physically-based Facial Asset for Production](https://mlsanity.com/paper/2202.05592/) (2022)
- [Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization](https://mlsanity.com/paper/2305.03043/) (2023)
- [Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance](https://mlsanity.com/paper/2501.05379/) (2025)
- [3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces](https://mlsanity.com/paper/2111.12448/) (2021)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)

## Abstract (arXiv)

Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering. We aim to maximize the variety of face identities, while increasing the robustness of correspondence between unique components, including middle-frequency geometry, albedo maps, specular intensity maps and high-frequency displacement details. Our deep learning based generative model learns to correlate albedo and geometry, which ensures the anatomical correctness of the generated assets. We demonstrate potential use of our generative model for novel identity generation, model fitting, interpolation, animation, high fidelity data visualization, and low-to-high resolution data domain transferring. We hope the release of this generative model will encourage further cooperation between all graphics, vision, and data focused professionals while demonstrating the cumulative value of every individual's complete biometric profile.