Difference between revisions of "Generative Facial Prior-Generative Adversarial Network (GFP-GAN)"
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[https://www.youtube.com/results?search_query=GFP-GAN YouTube search...] | [https://www.youtube.com/results?search_query=GFP-GAN YouTube search...] | ||
[https://www.google.com/search?q=GFP-GANg ...Google search] | [https://www.google.com/search?q=GFP-GANg ...Google search] | ||
| − | * [[Generative Adversarial Network (GAN)]] | + | * [[Attention]] Mechanism ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] |
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | ||
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]] ... [[Context]] | * [[Context-Conditional Generative Adversarial Network (CC-GAN)]] ... [[Context]] | ||
Revision as of 06:07, 13 August 2023
YouTube search... ...Google search
- Attention Mechanism ... Transformer ... Generative Pre-trained Transformer (GPT) ... GAN ... BERT
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN) ... Context
- Image-to-Image Translation
- Autoencoder (AE) / Encoder-Decoder
- Variational Autoencoder (VAE)
- Video/Image
- Generative AI ... Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Towards Real-World Blind Face Restoration with Generative Facial Prior Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
- Photo restoration with GFP-GAN Demo
- GFP-GAN Code
Framework that leverages the rich and diverse generative facial prior for the challenging blind face restoration task. This prior is incorporated into the restoration process with channel-split spatial feature transform layers, allowing us to achieve a good balance of realness and fidelity. Extensive comparisons demonstrate the superior capability of GFP-GAN in joint face restoration and color enhancement for real-world images, outperforming prior art. Conventional methods fine-tune an existing AI model to restore images by gauging differences between the artificial and real photos. That frequently leads to low-quality results, the scientists said. The new approach uses a pre-trained version of an existing model (NVIDIA's StyleGAN-2) to inform the team's own model at multiple stages during the image generation process. The technique aims to preserve the "identity" of people in a photo, with a particular focus on facial features like eyes and mouths.
