# GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors

arXiv: https://arxiv.org/abs/2203.07319 · HTML: https://mlsanity.com/paper/2203.07319/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 40 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Designing a Practical Degradation Model for Deep Blind Image Super-Resolution](https://mlsanity.com/paper/2103.14006/) (2021)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020)
- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [GAN Prior Embedded Network for Blind Face Restoration in the Wild](https://mlsanity.com/paper/2105.06070/) (2021)
- [Image Processing Using Multi-Code GAN Prior](https://mlsanity.com/paper/1912.07116/) (2019)
- [FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors](https://mlsanity.com/paper/1711.10703/) (2017)
- [GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution](https://mlsanity.com/paper/2012.00739/) (2020)
- [RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution](https://mlsanity.com/paper/1908.06382/) (2019)
- [Blind Face Restoration via Deep Multi-scale Component Dictionaries](https://mlsanity.com/paper/2008.00418/) (2020)
- [Progressive Semantic-Aware Style Transformation for Blind Face Restoration](https://mlsanity.com/paper/2009.08709/) (2020)
- [Deep Semantic Face Deblurring](https://mlsanity.com/paper/1803.03345/) (2018)
- [Progressive Face Super-Resolution via Attention to Facial Landmark](https://mlsanity.com/paper/1908.08239/) (2019)
- [HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment](https://mlsanity.com/paper/2005.05005/) (2020)
- [Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs](https://mlsanity.com/paper/1712.02765/) (2017)

## Built on by (most cited first, 30 of 39)

- [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023)
- [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023)
- [Low-Light Image Enhancement via Structure Modeling and Guidance](https://mlsanity.com/paper/2305.05839/) (2023)
- [Adversarial Diffusion Compression for Real-World Image Super-Resolution](https://mlsanity.com/paper/2411.13383/) (2024)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [SRGS: Super-Resolution 3D Gaussian Splatting](https://mlsanity.com/paper/2404.10318/) (2024)
- [Dual Associated Encoder for Face Restoration](https://mlsanity.com/paper/2308.07314/) (2023)
- [Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment](https://mlsanity.com/paper/2304.06440/) (2023)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [TextIR: A Simple Framework for Text-based Editable Image Restoration](https://mlsanity.com/paper/2302.14736/) (2023)
- [MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection](https://mlsanity.com/paper/2307.02733/) (2023)
- [StyleLipSync: Style-based Personalized Lip-sync Video Generation](https://mlsanity.com/paper/2305.00521/) (2023)
- [Show and Polish: Reference-Guided Identity Preservation in Face Video Restoration](https://mlsanity.com/paper/2507.10293/) (2025)
- [Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution](https://mlsanity.com/paper/2205.03777/) (2022)
- [Towards Real-world Video Face Restoration: A New Benchmark](https://mlsanity.com/paper/2404.19500/) (2024)
- [Surveillance Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2304.07580/) (2023)
- [FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration](https://mlsanity.com/paper/2311.15445/) (2023)
- [Out-of-domain GAN inversion via Invertibility Decomposition for Photo-Realistic Human Face Manipulation](https://mlsanity.com/paper/2212.09262/) (2022)
- [Efficient Hybrid Zoom using Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2401.01461/) (2024)
- [IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond](https://mlsanity.com/paper/2503.09294/) (2025)
- [Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration](https://mlsanity.com/paper/2603.29773/) (2026)
- [UGPNet: Universal Generative Prior for Image Restoration](https://mlsanity.com/paper/2401.00370/) (2023)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [End-to-end Face-swapping via Adaptive Latent Representation Learning](https://mlsanity.com/paper/2303.04186/) (2023)
- [Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation](https://mlsanity.com/paper/2303.05240/) (2023)
- [SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network](https://mlsanity.com/paper/2312.01999/) (2023)
- [Lipschitz-Driven Noise Robustness in VQ-AE for High-Frequency Texture Repair in ID-Specific Talking Heads](https://mlsanity.com/paper/2410.00990/) (2024)
- [Personalized Face Super-Resolution with Identity Decoupling and Fitting](https://mlsanity.com/paper/2508.10937/) (2025)
- [AvatarBack: Back-Head Generation for Complete 3D Avatars from Front-View Images](https://mlsanity.com/paper/2508.20623/) (2025)
- [Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior](https://mlsanity.com/paper/2205.14377/) (2022)

## Abstract (arXiv)

Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impressive results with the help of GAN prior. They either design complicated modules to modify the fixed GAN prior or adopt complex training strategies to finetune the generator. In this work, we propose a generative and controllable face SR framework, called GCFSR, which can reconstruct images with faithful identity information without any additional priors. Generally, GCFSR has an encoder-generator architecture. Two modules called style modulation and feature modulation are designed for the multi-factor SR task. The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor. The simple and elegant architecture can be trained from scratch in an end-to-end manner. For small upscaling factors (<=8), GCFSR can produce surprisingly good results with only adversarial loss. After adding L1 and perceptual losses, GCFSR can outperform state-of-the-art methods for large upscaling factors (16, 32, 64). During the test phase, we can modulate the generative strength via feature modulation by changing the conditional upscaling factor continuously to achieve various generative effects.