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DraGan

DragGAN: an innovative tool for precise image manipulation via interactive point dragging, enabling control over pose, shape, and more.

Image Generation Updated 8 minutes ago
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DraGan

DraGan's Top Features

Interactive point dragging
Feature-based motion supervision
New point tracking approach
Realistic image generation
Wide range of applications
Precise image control
GAN inversion for real images
Handles challenging scenarios
Applicable to various categories
Qualitative and quantitative superiority

Frequently asked questions about DraGan

DragGAN is an interactive tool for manipulating generated images by dragging points to specific target positions, allowing for precise control over pose, shape, expression, and layout.

Unlike existing methods that rely on manually annotated training data or prior 3D models, DragGAN offers flexible and precise control by allowing users to drag points directly on the image.

The key components of DragGAN include feature-based motion supervision and a new point tracking approach to keep localizing the handle points.

DragGAN can be applied to a wide range of categories, including animals, cars, humans, landscapes, and more.

Yes, DragGAN can produce realistic outputs even in challenging scenarios such as hallucinating occluded content and deforming shapes that follow the object's rigidity.

DragGAN operates on the learned generative image manifold of a GAN, enabling it to produce realistic images through precise manipulation techniques.

Yes, DragGAN can also be used for manipulating real images through a process known as GAN inversion.

DragGAN was developed by Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt, affiliated with institutions like the Max Planck Institute for Informatics and others.

DragGAN was introduced in the ACM SIGGRAPH 2023 Conference Proceedings.

More information on DragGAN can be found on their project webpage, where you can also find downloads for the paper and code.

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