# Learning Complete 3D Morphable Face Models from Images and Videos

arXiv: https://arxiv.org/abs/2010.01679 · HTML: https://mlsanity.com/paper/2010.01679/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 30 of 35)

- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations](https://mlsanity.com/paper/2203.14510/) (2022)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [Hand Avatar: Free-Pose Hand Animation and Rendering from Monocular Video](https://mlsanity.com/paper/2211.12782/) (2022)
- [REALY: Rethinking the Evaluation of 3D Face Reconstruction](https://mlsanity.com/paper/2203.09729/) (2022)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Learning a 3D Morphable Face Reflectance Model from Low-cost Data](https://mlsanity.com/paper/2303.11686/) (2023)
- [Interactive Face Video Coding: A Generative Compression Framework](https://mlsanity.com/paper/2302.09919/) (2023)
- [GANHead: Towards Generative Animatable Neural Head Avatars](https://mlsanity.com/paper/2304.03950/) (2023)
- [Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization](https://mlsanity.com/paper/2305.03043/) (2023)
- [Implicit Neural Head Synthesis via Controllable Local Deformation Fields](https://mlsanity.com/paper/2304.11113/) (2023)
- [TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction](https://mlsanity.com/paper/2502.10982/) (2025)
- [ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations](https://mlsanity.com/paper/2312.04028/) (2023)
- [Monocular and Generalizable Gaussian Talking Head Animation](https://mlsanity.com/paper/2504.00665/) (2025)
- [HQ3DAvatar: High Quality Controllable 3D Head Avatar](https://mlsanity.com/paper/2303.14471/) (2023)
- [Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space](https://mlsanity.com/paper/2504.21749/) (2025)
- [Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?](https://mlsanity.com/paper/2203.09824/) (2022)
- [GAN2X: Non-Lambertian Inverse Rendering of Image GANs](https://mlsanity.com/paper/2206.09244/) (2022)
- [Identity-Expression Ambiguity in 3D Morphable Face Models](https://mlsanity.com/paper/2109.14203/) (2021)
- [SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling](https://mlsanity.com/paper/2307.00804/) (2023)
- [VGGTFace: Topologically Consistent Facial Geometry Reconstruction in the Wild](https://mlsanity.com/paper/2511.20366/) (2025)
- [SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator](https://mlsanity.com/paper/2209.06423/) (2022)
- [High-Quality 3D Face Reconstruction with Affine Convolutional Networks](https://mlsanity.com/paper/2310.14237/) (2023)
- [TCFNet: Bidirectional face-bone transformation via a Transformer-based coarse-to-fine point movement network](https://mlsanity.com/paper/2508.14373/) (2025)
- [Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances](https://mlsanity.com/paper/2306.10006/) (2023)
- [Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map](https://mlsanity.com/paper/2308.13245/) (2023)
- [FaceLift: Semi-supervised 3D Facial Landmark Localization](https://mlsanity.com/paper/2405.19646/) (2024)
- [Building 3D Generative Models from Minimal Data](https://mlsanity.com/paper/2203.02554/) (2022)
- [DisControlFace: Adding Disentangled Control to Diffusion Autoencoder for One-shot Explicit Facial Image Editing](https://mlsanity.com/paper/2312.06193/) (2023)
- [3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior](https://mlsanity.com/paper/2404.16536/) (2024)

## Abstract (arXiv)

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity geometry, expressions and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches.