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Tuesday, May 23, 2023

Real Time Photo Editing from DragGAN

Has received significant praise,  plan to examine.

From YouTube:     https://www.youtube.com/watch?v=bQxfBE5n1oA

1,201 views  May 23, 2023  #draggan #ai

Introducing DragGAN: an AI Image Manipulation Tool for real-time photo editing. Developed by the renowned Max Planck Institute, DragGAN allows you to manipulate images in real-time by dragging and dropping points, resulting in stunning results. With its innovative features and versatile capabilities, DragGAN is set to redefine the world of AI photo editing.

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DragGAN: Google Researchers Unveil AI Technique for Magical Image Editing

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Yana Khare — Published On May 22, 2023 and Last Modified On May 23rd, 2023

Artificial Intelligence GANs Generative AI Image News

Researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL have recently released a new AI editing tool called DragGAN

Researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL have recently released a new AI technique. It allows users to manipulate images in seconds with just a click and drag. The new DragGAN is an AI editing tool that leverages a pre-trained GAN (Generative Adversarial Network) to synthesize ideas that precisely follow user input while remaining on the manifold of realistic images.

Learn More: An End-to-End Introduction to Generative Adversarial Networks(GANs) 

The Power of DragGAN

DragGAN is an interactive approach for intuitive point-based image editing far more powerful than Photoshop’s Warp tool. Unlike Photoshop, which merely smushes pixels around, DragGAN uses AI to regenerate the underlying object. With DragGAN, users can rotate images as if they were 3D, change the dimensions of cars, manipulate smiles into frowns, and adjust reflections on lakes. Moreover, they can change the direction someone faces.

Also Read: How to Use Generative AI to Create Beautiful Pictures for Free?

General Framework and Optimisation of Latent Codes

What sets DragGAN apart from other approaches is its general framework which does not rely on domain-specific modeling or auxiliary networks. To achieve this, the researchers used an optimization of latent codes that incrementally moved multiple handle points toward their target locations alongside a point-tracking procedure to trace the trajectory of the handle points faithfully. Both components use the discriminative quality of intermediate feature maps of the GAN to yield pixel-precise image deformations and interactive performance.

Outperforming SOTA in GAN-Based Manipulation

DragGAN uses Generative Adversarial Network to edit images while ensuring that they remain realistic | AI

According to the researchers, DragGAN by Google outperforms the state-of-the-art (SOTA) in GAN-based manipulation. Furthermore, it opens new directions for powerful image editing using generative priors. They look to extend point-based editing to  .. .' 

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