Difference between revisions of "Generative Facial Prior-Generative Adversarial Network (GFP-GAN)"
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* [[Variational Autoencoder (VAE)]] | * [[Variational Autoencoder (VAE)]] | ||
* [[Generated Image]] | * [[Generated Image]] | ||
| + | * [[Generative AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]] | ||
* [http://arxiv.org/pdf/2101.04061.pdf Towards Real-World Blind Face Restoration with Generative Facial Prior Xintao Wang, Yu Li, Honglun Zhang, Ying Shan] | * [http://arxiv.org/pdf/2101.04061.pdf Towards Real-World Blind Face Restoration with Generative Facial Prior Xintao Wang, Yu Li, Honglun Zhang, Ying Shan] | ||
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* [http://app.baseten.co/apps/QPp4nPE/operator_views/RqgOnqV Photo restoration with GFP-GAN Demo] | * [http://app.baseten.co/apps/QPp4nPE/operator_views/RqgOnqV Photo restoration with GFP-GAN Demo] | ||
* [http://github.com/TencentARC/GFPGAN GFP-GAN Code] | * [http://github.com/TencentARC/GFPGAN 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 | + | 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. |
| − | 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. | ||
Revision as of 15:16, 8 March 2023
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- Generative Adversarial Network (GAN)
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
- Image-to-Image Translation
- Autoencoder (AE) / Encoder-Decoder
- Variational Autoencoder (VAE)
- Generated Image
- Generative AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's BingAI ... You ...Google's Bard
- 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.
