# 3D Face Morphable Models "In-the-Wild"

arXiv: https://arxiv.org/abs/1701.05360 · HTML: https://mlsanity.com/paper/1701.05360/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 30 of 44)

- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction](https://mlsanity.com/paper/2107.12512/) (2021)
- [Learning Neural Parametric Head Models](https://mlsanity.com/paper/2212.02761/) (2022)
- [A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images](https://mlsanity.com/paper/2302.14434/) (2023)
- [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)
- [Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement](https://mlsanity.com/paper/2106.11423/) (2021)
- [FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction](https://mlsanity.com/paper/2111.01082/) (2021)
- [Text and Image Guided 3D Avatar Generation and Manipulation](https://mlsanity.com/paper/2202.06079/) (2022)
- [Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/2105.07474/) (2021)
- [DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image](https://mlsanity.com/paper/2204.03688/) (2022)
- [3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation](https://mlsanity.com/paper/2209.05434/) (2022)
- [InsTaG: Learning Personalized 3D Talking Head from Few-Second Video](https://mlsanity.com/paper/2502.20387/) (2025)
- [Inverting Generative Adversarial Renderer for Face Reconstruction](https://mlsanity.com/paper/2105.02431/) (2021)
- [Self-Supervised 3D Face Reconstruction via Conditional Estimation](https://mlsanity.com/paper/2110.04800/) (2021)
- [Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation](https://mlsanity.com/paper/2303.01311/) (2023)
- [3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling](https://mlsanity.com/paper/2209.07366/) (2022)
- [DPHMs: Diffusion Parametric Head Models for Depth-based Tracking](https://mlsanity.com/paper/2312.01068/) (2023)
- [SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery](https://mlsanity.com/paper/2108.05465/) (2021)
- [PhoMoH: Implicit Photorealistic 3D Models of Human Heads](https://mlsanity.com/paper/2212.07275/) (2022)
- [3D human tongue reconstruction from single "in-the-wild" images](https://mlsanity.com/paper/2106.12302/) (2021)
- [Locally Adaptive Neural 3D Morphable Models](https://mlsanity.com/paper/2401.02937/) (2024)
- [FaceTopoNet: Facial Expression Recognition using Face Topology Learning](https://mlsanity.com/paper/2209.06322/) (2022)
- [Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction](https://mlsanity.com/paper/2106.08148/) (2021)
- [Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation](https://mlsanity.com/paper/2303.15892/) (2023)
- [FLAME-based Multi-View 3D Face Reconstruction](https://mlsanity.com/paper/2308.07551/) (2023)
- [FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer](https://mlsanity.com/paper/2601.04520/) (2026)
- [3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs](https://mlsanity.com/paper/2203.04643/) (2022)
- [A Review of 3D Face Reconstruction From a Single Image](https://mlsanity.com/paper/2110.09299/) (2021)
- [Learning to Deblur and Rotate Motion-Blurred Faces](https://mlsanity.com/paper/2112.07599/) (2021)

## Abstract (arXiv)

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions ("in-the-wild"). In this paper, we propose the first, to the best of our knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an "in-the-wild" texture model. We show that the employment of such an "in-the-wild" texture model greatly simplifies the fitting procedure, because there is no need to optimize with regards to the illumination parameters. Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary images. Finally, we have captured the first 3D facial database with relatively unconstrained conditions and report quantitative evaluations with state-of-the-art performance. Complementary qualitative reconstruction results are demonstrated on standard "in-the-wild" facial databases. An open source implementation of our technique is released as part of the Menpo Project.