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

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

18 May 2023arXiv:2305.10973archive 2025-07-28

Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt

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.

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Syntology Ran 7 of 16 code samples harvested from 3 repositories linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 3 ran · fixture could not drive it.

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XingangPan/DragGAN officialmentioned on GitHubpytorch report
MICV-yonsei/DragText mentioned on GitHubpytorchApache-2.0 report
cuixing100876/instastyle mentioned on GitHubpytorch report
opengvlab/internchat mentioned on GitHubpytorchApache-2.0 report
opengvlab/interngpt mentioned on GitHubpytorch report
saic-mdal/HiDT mentioned on GitHubpytorchNOASSERTION report

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1ran · honoured contract
3ran · our draft was wrong
3ran · fixture could not drive it
9unverified

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clear_state XingangPan/DragGAN/visualizer_drag_gradio.py official repository ran · our draft was wrong licence not identified · pointer only · 98587987bd524213 · report
reverse_point_pairs XingangPan/DragGAN/visualizer_drag_gradio.py official repository ran · honoured contract licence not identified · pointer only · 85ecb3cb5eeb8bc5 · report
HWC3 opengvlab/internchat/iGPT/models/image.py community (archive-listed) ran · fixture could not drive it Apache-2.0 (permissive) · f9ec7d70add02b6f · report
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Tasks

Image ManipulationPoint Tracking

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