GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Frequently Asked Questions can be found in FAQ.md. 🚩 Updates. Add RestoreFormer inference codes.

We calculate the standard GAN loss from the discriminators (DS and DR). For the eye gaze consistency (L_eg) and landmark losses (L_lm), we trained two additional Convolutional Neural Networks. The identity-transform loss (L_id) can directly be calculated. The cycle-consistency loss (L_cyc) is being omitted in order to reduce cluttering.

GFP-GAN由去退化模块，作为先验的预训练GAN (StyleGAN2)组成，然后用基于通道分割的空间特征转换（CS-SFT）方式连接起来。 损失函数除了传统的对抗损失之外，还添加了面部组件损失、以及身份保真损失、以及去退化模块上的重建损失。 Degradation Removal Module 去退化模块. 去退化模块为一个UNet，以减轻后续模块的压力。 在UNet解码器的每个分辨率尺度输出图像，使用L1损失函数监督去退化模块。 Generative Facial Prior and Latent Code Mapping 生成式面 …

2021年2月25日 · In this work, we propose a method to improve protein function prediction using their sequences by deeply extracting relationships between Gene Ontology terms. To this end, we construct a conditional generative adversarial network which helps to effectively discover and incorporate term correlations in the annotation process.

2023年11月7日 · gfp-gan利用丰富和多样化的先验封装在预训练的脸gan盲脸恢复。通过空间特征变换层，将生成式面部先验(gfp)融合到人脸恢复过程中，使人脸恢复过程更容易实现论文说，之前的研究gan中，。

2020年4月25日 · We leverage a coupled generative adversarial network (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain.

2022年9月16日 · GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs.

2020年9月28日 · We leverage a coupled generative adversarial network (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cp-GAN framework consists of two GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain.

2021年2月25日 · In this work, we propose a method to improve protein function prediction using their sequences by deeply extracting relationships between Gene Ontology terms. To this end, we construct a...

Official TP-GAN Tensorflow implementation for the ICCV17 paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis" by Huang, Rui and Zhang, Shu and Li, Tianyu and He, Ran. The goal is to recover a frontal face image of the same person from a single face image under any poses.