# Cross-Spectral Face Hallucination via Disentangling Independent Factors

arXiv: https://arxiv.org/abs/1909.04365 · HTML: https://mlsanity.com/paper/1909.04365/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2209.08814/) (2022)
- [Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer](https://mlsanity.com/paper/2303.17783/) (2023)
- [Towards NIR-VIS Masked Face Recognition](https://mlsanity.com/paper/2104.06761/) (2021)
- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement](https://mlsanity.com/paper/2206.04854/) (2022)
- [Physically-Based Face Rendering for NIR-VIS Face Recognition](https://mlsanity.com/paper/2211.06408/) (2022)
- [Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition](https://mlsanity.com/paper/2204.11434/) (2022)
- [L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation](https://mlsanity.com/paper/2507.02619/) (2025)
- [Thermal to Visible Image Synthesis under Atmospheric Turbulence](https://mlsanity.com/paper/2204.03057/) (2022)
- [Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation](https://mlsanity.com/paper/2605.04769/) (2026)
- [Styleverse: Towards Identity Stylization across Heterogeneous Domains](https://mlsanity.com/paper/2203.00861/) (2022)
- [LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images](https://mlsanity.com/paper/2503.16376/) (2025)

## Abstract (arXiv)

The cross-sensor gap is one of the challenges that have aroused much research interests in Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the gap with deep generative networks, most of them suffer from the inevitable misalignment between different face modalities. Instead of imaging sensors, the misalignment primarily results from facial geometric variations that are independent of the spectrum. Rather than building a monolithic but complex structure, this paper proposes a Pose Aligned Cross-spectral Hallucination (PACH) approach to disentangle the independent factors and deal with them in individual stages. In the first stage, an Unsupervised Face Alignment (UFA) module is designed to align the facial shapes of the near-infrared (NIR) images with those of the visible (VIS) images in a generative way, where UV maps are effectively utilized as the shape guidance. Thus the task of the second stage becomes spectrum translation with aligned paired data. We develop a Texture Prior Synthesis (TPS) module to achieve complexion control and consequently generate more realistic VIS images than existing methods. Experiments on three challenging NIR-VIS datasets verify the effectiveness of our approach in producing visually appealing images and achieving state-of-the-art performance in HFR.