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Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

The Power of Control

Creating visual content that aligns with user specifications necessitates the ability to control aspects such as pose, shape, expression, and layout of the generated objects. Existing GAN control methodologies typically rely on manually annotated training data or prior 3D models, which can limit flexibility, precision, and general applicability.


The authors, however, explore a far more dynamic method of controlling GANs: the ability to "drag" points within an image to specific target locations interactively.

Link to paper:

Introducing Drag Your GAN

To achieve this interactive point-based manipulation, the researchers propose Drag Your GAN, a two-component system that offers users unprecedented control over image manipulation.

The first component of Drag Your GAN is the feature-based motion supervision. This element actively drives the selected point - referred to as the handle point - towards a target position determined by the user. This control offers precise manipulation of the image, allowing for exact modifications as required.

The second component is a novel point tracking approach. By leveraging the discriminative generator features of the GAN, it continuously localizes the handle point's position, ensuring accuracy in image manipulation.

Unleashing Potential with Drag Your GAN

Drag Your GAN allows users to deform an image, offering precise control over pixel placement and thus facilitating manipulations of pose, shape, expression, and layout across a wide range of categories including animals, cars, humans, landscapes, and more.

As these transformations occur within the learned generative image manifold of a GAN, the outputs remain realistic even in challenging scenarios, such as visualizing occluded content or deforming shapes in a way that adheres to the object's inherent rigidity.

A Step Forward

The advantage of Drag Your GAN over prior methodologies is demonstrated through both qualitative and quantitative comparisons, specifically in the realms of image manipulation and point tracking. This research also highlights how Drag Your GAN can manipulate real images through GAN inversion, showcasing the potential applications and effectiveness of this innovative approach.



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