# Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets

arXiv: https://arxiv.org/abs/2011.00912 · HTML: https://mlsanity.com/paper/2011.00912/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

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

- [AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2105.00402/) (2021)
- [Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT images](https://mlsanity.com/paper/2107.04062/) (2021)
- [Controllable Face Manipulation and UV Map Generation by Self-supervised Learning](https://mlsanity.com/paper/2209.12050/) (2022)
- [Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet](https://mlsanity.com/paper/2607.21840/) (2026)

## Abstract (arXiv)

Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.