# UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition

arXiv: https://arxiv.org/abs/1712.04695 · HTML: https://mlsanity.com/paper/1712.04695/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

## Built on by (most cited first, 30 of 62)

- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction](https://mlsanity.com/paper/2211.13874/) (2022)
- [Text and Image Guided 3D Avatar Generation and Manipulation](https://mlsanity.com/paper/2202.06079/) (2022)
- [Relightify: Relightable 3D Faces from a Single Image via Diffusion Models](https://mlsanity.com/paper/2305.06077/) (2023)
- [Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/2105.07474/) (2021)
- [VariTex: Variational Neural Face Textures](https://mlsanity.com/paper/2104.05988/) (2021)
- [Facial Geometric Detail Recovery via Implicit Representation](https://mlsanity.com/paper/2203.09692/) (2022)
- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [Controllable and Guided Face Synthesis for Unconstrained Face Recognition](https://mlsanity.com/paper/2207.10180/) (2022)
- [Towards Realistic Generative 3D Face Models](https://mlsanity.com/paper/2304.12483/) (2023)
- [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)
- [MeInGame: Create a Game Character Face from a Single Portrait](https://mlsanity.com/paper/2102.02371/) (2021)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration](https://mlsanity.com/paper/2306.06513/) (2023)
- [Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion](https://mlsanity.com/paper/2504.00430/) (2025)
- [KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2201.07394/) (2022)
- [BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction](https://mlsanity.com/paper/2209.09029/) (2022)
- [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://mlsanity.com/paper/2208.14263/) (2022)
- [UV-Based 3D Hand-Object Reconstruction with Grasp Optimization](https://mlsanity.com/paper/2211.13429/) (2022)
- [Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition](https://mlsanity.com/paper/2302.13279/) (2023)
- [POCE: Pose-Controllable Expression Editing](https://mlsanity.com/paper/2304.08938/) (2023)
- [Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture](https://mlsanity.com/paper/2406.00440/) (2024)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [Makeup Prior Models for 3D Facial Makeup Estimation and Applications](https://mlsanity.com/paper/2403.17761/) (2024)
- [FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy](https://mlsanity.com/paper/2503.17197/) (2025)
- [Unsupervised High-Fidelity Facial Texture Generation and Reconstruction](https://mlsanity.com/paper/2110.04760/) (2021)
- [VGG-Tex: A Vivid Geometry-Guided Facial Texture Estimation Model for High Fidelity Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2409.09740/) (2024)
- [TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates](https://mlsanity.com/paper/2204.03671/) (2022)

## Abstract (arXiv)

Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.