# Copy and Paste GAN: Face Hallucination from Shaded Thumbnails

arXiv: https://arxiv.org/abs/2002.10650 · HTML: https://mlsanity.com/paper/2002.10650/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [IFQA: Interpretable Face Quality Assessment](https://mlsanity.com/paper/2211.07077/) (2022)
- [GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond](https://mlsanity.com/paper/2207.14812/) (2022)
- [Joint Face Image Restoration and Frontalization for Recognition](https://mlsanity.com/paper/2105.09907/) (2021)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset](https://mlsanity.com/paper/2110.05031/) (2021)
- [Unsupervised Image Decomposition with Phase-Correlation Networks](https://mlsanity.com/paper/2110.03473/) (2021)
- [Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar](https://mlsanity.com/paper/2103.08863/) (2021)
- [Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails](https://mlsanity.com/paper/2108.00602/) (2021)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [Universal Face Restoration With Memorized Modulation](https://mlsanity.com/paper/2110.01033/) (2021)
- [VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots](https://mlsanity.com/paper/2105.14954/) (2021)

## Abstract (arXiv)

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternately in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.