LINK TO PAPER:
https://huggingface.co/papers/2305.10973

The paper proposes DragGAN, an interactive approach for manipulating images generated by generative adversarial networks (GANs). DragGAN allows users to specify handle points and target points on an image and moves the handle points to precisely reach the target points. This enables control of spatial attributes like pose, shape, expression, and layout.

DragGAN has two main components:

1. Feature-based motion supervision: This optimizes the latent code of the GAN to move the handle points closer to the targets. Each optimization step shifts the handle points toward the targets.

2. Point tracking: This leverages the discriminative features of the GAN generator to keep localizing the positions of the handle points. The optimization process is repeated until the handle points reach the targets.

DragGAN also allows users to optionally specify a region of interest to constrain the manipulation to that region. DragGAN can perform manipulations efficiently without relying on additional networks. This allows for interactive editing sessions where users can quickly explore different layouts.

DragGAN is evaluated on datasets of animals, humans, cars, and landscapes. It can effectively move user-specified handle points to target points, enabling diverse manipulations across categories. DragGAN can hallucinate occluded content and deform images while preserving object structure. DragGAN also outperforms the state-of-the-art point tracking method RAFT. By combining with GAN inversion, DragGAN can also edit real images.

In summary, the key contributions of the paper are:

1. Proposing DragGAN, an interactive approach for manipulating GAN-generated images by specifying handle and target points.

2. Feature-based motion supervision and point-tracking components that enable precise control of spatial attributes.

3. Allowing region-specific editing and efficient manipulation without relying on additional networks.

4. Evaluating DragGAN on diverse datasets and showing its advantages over prior work.

5. Extending DragGAN to edit real images through GAN inversion.

The approach enables controllable editing of generated images through the following process:

1. Users specify handle points and target points on an initial GAN-generated image. Optionally, they can also draw a mask denoting the movable region.

2. Motion supervision: An optimization step is performed to drive the handle points toward the target points by updating the latent code of the GAN. This results in a slightly changed image with the object moved.

3. Point tracking: The handle points are updated to track the object in the new image. This is done by finding the feature in the new image that is closest to the feature at the original handle points.

4. Steps 2 and 3 are repeated until the handle points reach the target points.

5. The final image with the object manipulated as specified by the user is output.

The key insight is that the feature spaces of GANs are discriminative enough to enable point tracking without additional neural networks or tracking models. This allows the approach to run efficiently and support interactive editing. Experiments show that this simple method outperforms state-of-the-art point-tracking approaches.

• The method supervises the motion of handle points on a GAN-generated image via a shifted patch loss on the intermediate feature maps of the generator.

• To move a handle point to a target point, it supervises a small patch around the handle point to move towards the target point.

• It tracks the handle points on the feature maps via nearest neighbor search to update their positions. This is necessary to ensure the correct points are supervised in the next step.

• The optimization process continues until the handle points reach the target points. The user can stop at any step and continue editing with new points.

• The method only updates the latent code for the first 6 layers to mainly affect the spatial attributes. The remaining layers are fixed to preserve the appearance.

• For point tracking, it searches for the nearest neighbor of the initial handle point feature in a patch around its position. This effectively tracks the points without needing additional models.

• Experiments show the method achieves more natural and superior results than baselines on various datasets. It allows for interactive image manipulation with only a few seconds of wait between edits.

The key ideas are:

1) Use a simply shifted patch loss on GAN features to supervise point motion;

2) Perform point tracking via nearest neighbor search in feature space to avoid accumulation error;

3) Selectively optimize latent code for controllability and efficiency.

• Our approach leverages a pre-trained GAN to synthesize images that precisely follow user input while staying on the manifold of realistic images.

• Unlike previous methods, our approach does not rely on dataset-specific priors and can be applied to various image domains.

• We propose a novel point-based interface that directly specifies the target spatial configuration of image regions. The user drags handle points in the image to manipulate the layout, pose, and shape.

• To achieve controllable image synthesis, we propose a point-tracking module and a motion supervision module. The point-tracking module learns to track handle points through the image generation process. The motion supervision module utilizes optical flow to supervise the latent code optimization so that the synthesized image follows the handle point motion.

• Experiments show that our approach can generate high-quality and controllable images on various datasets. It significantly outperforms previous methods in quantitative evaluations.

• Our approach has extrapolation capability to some extent but is still limited by the diversity of the training data. The editing quality can degrade for poses and shapes that deviate much from the training distribution.

• As our method can manipulate the spatial attributes of images, it could be misused to create fake images of real people. Any application of this method needs to respect privacy regulations.

The paper introduces a point-based manipulation method for deforming and editing images synthesized by GANs. The key components are:

1. An optimization procedure that incrementally moves multiple control points to their target locations while preserving structural and textural details. This uses the discriminative power of the GAN's intermediate feature maps.

2. A point tracking method to trace the trajectories of the control points. This also utilizes the GAN's feature maps to yield pixel-precise deformations and interactive performance.

The method outperforms prior GAN-based editing techniques and could be extended to 3D GANs. It allows powerful image editing using the generative priors learned by GANs.

The main contributions are:

1. An optimization method to manipulate GAN-generated images by moving control points.

2. A point tracking procedure to follow the trajectories of the control points.

3. Utilizing the GAN's intermediate feature maps for precise image deformations and interactivity.

4. State-of-the-art results in editing and manipulating GAN-synthesized images.

The work demonstrates how GANs can be used as strong priors for image editing and manipulation. The point-based interface provides an intuitive way for users to deform and edit GAN-generated images.

The paper "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold" by Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Anthropic, PBC, and Christian Theobalt proposes an interactive point-based manipulation method for controllable image generation and editing using generative adversarial networks (GANs).

Their method allows users to manipulate images by dragging control points, which are tracked to match the image content. Motion supervision with the optical flow is used to guide the GAN to generate plausible continuations of the editing process. This allows for realistic image manipulations even for large displacements of control points.

Experiments show that their method can generate high-quality image edits and continuations for a variety of image datasets and types of control points. Both qualitative and quantitative evaluations demonstrate superior performance over baselines.

The key contributions are:

1. An interactive point-based manipulation framework for controllable image generation using GANs.

2. Motion supervision with the optical flow to guide the GAN.

3. A point tracking method to match control points to image content.

4. Extensive experiments on various datasets and applications like image editing, landmark manipulation, and video generation.

The method has some limitations in handling out-of-distribution poses and low-textured image regions but shows promising results for interactive and controllable image generation with GANs.

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