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.

本文介绍了一种基于 GAN 先验的面部图像超分辨率方法，通过特征调制和风格调制生成更加真实的面部细节并保留识别信息，对比现有方法，该方法可以在不需要额外先验的情况下从头开始训练，并可在测试时通过特征调制实现不同的生成效果。