Drag Your GAN: Interactive Point-based Manipulation on the Generative
  Image Manifold
Synthesizing visual content that meets users' needs often requires flexible
and precise controllability of the pose, shape, expression, and layout of the
generated objects. Existing approaches gain controllability of generative
adversarial networks (GANs) via manually annotated training data or a prior 3D
model, which often lack flexibility, precision, and generality. In this work,
we study a powerful yet much less explored way of controlling GANs, that is, to
"drag" any points of the image to precisely reach target points in a
user-interactive manner, as shown in Fig.1. To achieve this, we propose
DragGAN, which consists of two main components: 1) a feature-based motion
supervision that drives the handle point to move towards the target position,
and 2) a new point tracking approach that leverages the discriminative
generator features to keep localizing the position of the handle points.
Through DragGAN, anyone can deform an image with precise control over where
pixels go, thus manipulating the pose, shape, expression, and layout of diverse
categories such as animals, cars, humans, landscapes, etc. As these
manipulations are performed on the learned generative image manifold of a GAN,
they tend to produce realistic outputs even for challenging scenarios such as
hallucinating occluded content and deforming shapes that consistently follow
the object's rigidity. Both qualitative and quantitative comparisons
demonstrate the advantage of DragGAN over prior approaches in the tasks of
image manipulation and point tracking. We also showcase the manipulation of
real images through GAN inversion.
64670317cdea48159aa5d7a9
Xingang Pan
claimed_verified
2023-05-19T11:11:53.469Z
646758d6a9b4610868a138ff
https://cdn-avatars.huggingface.co/v1/production/uploads/646758d6a9b4610868a138ff/fNBlK5g4g7vlPAR1bQimZ.png
Xingang Pan
user
Xingang-Pan
64670317cdea48159aa5d7aa
Ayush Tewari
extracted_pending
2023-05-19T05:03:20.900Z
6386e304b3ef0523c6419a05
/avatars/3e6d292962dd27c8b299e3c96697061b.svg
Ayush Tewari
user
ayusht
64670317cdea48159aa5d7ab
Thomas Leimkühler
64670317cdea48159aa5d7ac
Lingjie Liu
64670317cdea48159aa5d7ad
Abhimitra Meka
64670317cdea48159aa5d7ae
Christian Theobalt
Drag Your GAN: Interactive Point-based Manipulation on the Generative
  Image Manifold
Synthesizing visual content that meets users' needs often requires flexible
and precise controllability of the pose, shape, expression, and layout of the
generated objects. Existing approaches gain controllability of generative
adversarial networks (GANs) via manually annotated training data or a prior 3D
model, which often lack flexibility, precision, and generality. In this work,
we study a powerful yet much less explored way of controlling GANs, that is, to
"drag" any points of the image to precisely reach target points in a
user-interactive manner, as shown in Fig.1. To achieve this, we propose
DragGAN, which consists of two main components: 1) a feature-based motion
supervision that drives the handle point to move towards the target position,
and 2) a new point tracking approach that leverages the discriminative
generator features to keep localizing the position of the handle points.
Through DragGAN, anyone can deform an image with precise control over where
pixels go, thus manipulating the pose, shape, expression, and layout of diverse
categories such as animals, cars, humans, landscapes, etc. As these
manipulations are performed on the learned generative image manifold of a GAN,
they tend to produce realistic outputs even for challenging scenarios such as
hallucinating occluded content and deforming shapes that consistently follow
the object's rigidity. Both qualitative and quantitative comparisons
demonstrate the advantage of DragGAN over prior approaches in the tasks of
image manipulation and point tracking. We also showcase the manipulation of
real images through GAN inversion.
DragGAN enables precise user-controlled manipulation of image content through interactive point dragging, improving realism and flexibility over existing GAN-based methods.
generative adversarial networks feature-based motion supervision point tracking discriminative generator features generative image manifold image manipulation GAN inversion