# VGGTFace: Topologically Consistent Facial Geometry Reconstruction in the Wild

arXiv: https://arxiv.org/abs/2511.20366 · HTML: https://mlsanity.com/paper/2511.20366/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 26 of 26)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [General Facial Representation Learning in a Visual-Linguistic Manner](https://mlsanity.com/paper/2112.03109/) (2021)
- [NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads](https://mlsanity.com/paper/2305.03027/) (2023)
- [H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction](https://mlsanity.com/paper/2107.12512/) (2021)
- [FML: Face Model Learning from Videos](https://mlsanity.com/paper/1812.07603/) (2018)
- [SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://mlsanity.com/paper/2402.18848/) (2024)
- [Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatars](https://mlsanity.com/paper/2502.20220/) (2025)
- [MVF-Net: Multi-View 3D Face Morphable Model Regression](https://mlsanity.com/paper/1904.04473/) (2019)
- [A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images](https://mlsanity.com/paper/2302.14434/) (2023)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [Learning Complete 3D Morphable Face Models from Images and Videos](https://mlsanity.com/paper/2010.01679/) (2020)
- [Practical Face Reconstruction via Differentiable Ray Tracing](https://mlsanity.com/paper/2101.05356/) (2021)
- [Instant Multi-View Head Capture through Learnable Registration](https://mlsanity.com/paper/2306.07437/) (2023)
- [Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection](https://mlsanity.com/paper/2106.07852/) (2021)
- [Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function](https://mlsanity.com/paper/2201.01016/) (2022)
- [High-Quality Facial Geometry and Appearance Capture at Home](https://mlsanity.com/paper/2312.03442/) (2023)
- [Implicit Shape and Appearance Priors for Few-Shot Full Head Reconstruction](https://mlsanity.com/paper/2310.08784/) (2023)
- [RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs](https://mlsanity.com/paper/2302.05486/) (2023)
- [High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse Rendering](https://mlsanity.com/paper/2401.08398/) (2024)
- [GRAPE: Generalizable and Robust Multi-view Facial Capture](https://mlsanity.com/paper/2407.10193/) (2024)
- [Facial Appearance Capture at Home with Patch-Level Reflectance Prior](https://mlsanity.com/paper/2506.03478/) (2025)

## Built on by (most cited first, 4 of 4)

- [Face Anything: 4D Face Reconstruction from Any Image Sequence](https://mlsanity.com/paper/2604.19702/) (2026)
- [Learning a Delighting Prior for Facial Appearance Capture in the Wild](https://mlsanity.com/paper/2605.05636/) (2026)
- [GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion](https://mlsanity.com/paper/2606.27659/) (2026)
- [UVFaceFusion: Fast Multi-view Topologically Consistent Face Reconstruction in the Wild via UV-space Neural Fusion](https://mlsanity.com/paper/2607.18798/) (2026)

## Abstract (arXiv)

Reconstructing topologically consistent facial geometry is crucial for the digital avatar creation pipelines. Existing methods either require tedious manual efforts, lack generalization to in-the-wild data, or are constrained by the limited expressiveness of 3D Morphable Models. To address these limitations, we propose VGGTFace, an automatic approach that innovatively applies the 3D foundation model, i.e. VGGT, for topologically consistent facial geometry reconstruction from in-the-wild multi-view images captured by everyday users. Our key insight is that, by leveraging VGGT, our method naturally inherits strong generalization ability and expressive power from its large-scale training and point map representation. However, it is unclear how to reconstruct a topologically consistent mesh from VGGT, as the topology information is missing in its prediction. To this end, we augment VGGT with Pixel3DMM for injecting topology information via pixel-aligned UV values. In this manner, we convert the pixel-aligned point map of VGGT to a point cloud with topology. Tailored to this point cloud with known topology, we propose a novel Topology-Aware Bundle Adjustment strategy to fuse them, where we construct a Laplacian energy for the Bundle Adjustment objective. Our method achieves high-quality reconstruction in 10 seconds for 16 views on a single NVIDIA RTX 4090. Experiments demonstrate state-of-the-art results on benchmarks and impressive generalization to in-the-wild data. Code is available at https://github.com/grignarder/vggtface.