Summary Drag Your GAN Interactive Point-based Manipulation arxiv.org
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The paper discusses a technique for interactive point-based manipulation of GAN-generated images using a new point tracking approach that outperforms existing methods and achieves efficient manipulation in a few seconds on a single RTX 3090 GPU.
Key Points
- DragGAN is a tool for interactive image manipulation and point tracking using GANs.
- The approach consists of feature-based motion supervision and a new point tracking approach.
- The tool includes a GAN-based point tracking algorithm that outperforms existing approaches.
- The method allows fine-grained control over spatial attributes using point-based editing.
- The approach achieves natural and superior results on various datasets.
- The method involves optimizing the latent code to achieve manipulations while preserving image distribution.
Summaries
281 word summary
DragGAN is a tool that uses GANs for interactive image manipulation and point tracking, allowing users to adjust position, shape, expression, and body pose of diverse object categories. It includes a GAN-based point tracking algorithm that outperforms existing approaches and achieves efficient manipulation in a few seconds on a single RTX 3090 GPU. The technique possesses flexibility, precision, and generality to interactively modify the shape, position, and layout of generated objects. Future work includes extending point-based editing to 3D generative models and adhering to privacy regulations. The approach largely pushes the upper bound of the task while maintaining a comfortable running time for users.
The paper discusses a technique for interactive point-based manipulation of GAN-generated images using StyleGAN2. The proposed approach allows fine-grained control over spatial attributes using point-based editing and enables local semantics transferring between different samples. The method outperforms state-of-the-art point tracking approaches and handles long-range tracking better than previous methods.
The article presents a method for interactive point-based manipulation of GAN-generated images using GAN inversion and a new point tracking approach. The method is evaluated quantitatively and qualitatively with comparisons to other methods, showing accurate tracking of handle points and achieving diverse and natural manipulation effects. The approach outperforms other methods in terms of tracking accuracy and image quality, as indicated by the FID score, and is robust under different numbers of handle points.
A new method for interactive point-based manipulation using GANs is discussed in the paper. It involves optimizing the latent code to achieve manipulations while preserving image distribution, and W space allows for out-of-distribution manipulations. The method works well for dense keypoint cases, and the paper includes a qualitative comparison and real image manipulation examples.
398 word summary
A new method for interactive point-based manipulation using GANs is discussed in the paper. It involves optimizing the latent code to achieve manipulations while preserving image distribution, and W space allows for out-of-distribution manipulations. The method works well for dense keypoint cases, and the paper includes a qualitative comparison and real image manipulation examples.
The article presents a method for interactive point-based manipulation of GAN-generated images using GAN inversion and a new point tracking approach. The method is evaluated quantitatively and qualitatively with comparisons to other methods, showing accurate tracking of handle points and achieving diverse and natural manipulation effects. The approach outperforms other methods in terms of tracking accuracy and image quality, as indicated by the FID score, and is robust under different numbers of handle points.
The paper discusses a technique for interactive point-based manipulation of GAN-generated images using StyleGAN2. The proposed approach allows fine-grained control over spatial attributes using point-based editing and enables local semantics transferring between different samples. The method outperforms state-of-the-art point tracking approaches and handles long-range tracking better than previous methods.
Future work includes extending point-based editing to 3D generative models. Privacy regulations must be adhered to and texture-rich handle points are suggested. A binary mask can be used to reduce ambiguity in the manipulation. The approach largely pushes the upper bound of the task while maintaining a comfortable running time for users. DragGAN is a new tool that uses generative adversarial networks (GANs) for interactive image manipulation and point tracking. It allows users to adjust position, shape, expression, and body pose of diverse object categories such as animals, humans, cars, and landscapes. The tool includes a GAN-based point tracking algorithm that outperforms existing approaches and achieves efficient manipulation in a few seconds on a single RTX 3090 GPU. The approach consists of two main components: feature-based motion supervision and a new point tracking approach that leverages discriminative generator features. Users only need to click a few handle and target points on the image, and the approach will move the handle points to precisely reach their corresponding target points. The technique possesses flexibility, precision, and generality to interactively modify the shape, position, and layout of generated objects. It allows users to control any number of handle and target points on the image, making it applicable to different object categories. The tool is evaluated on diverse datasets and offers live, interactive editing sessions.
568 word summary
DragGAN is a new tool for interactive image manipulation and point tracking using generative adversarial networks (GANs). It allows users to adjust position, shape, expression, and body pose of diverse object categories such as animals, humans, cars, and landscapes. The approach consists of two main components: feature-based motion supervision and a new point tracking approach that leverages discriminative generator features. Users only need to click a few handle and target points on the image, and the approach will move the handle points to precisely reach their corresponding target points. DragGAN includes a GAN-based point tracking algorithm that outperforms existing approaches and achieves efficient manipulation in a few seconds on a single RTX 3090 GPU. The tool is evaluated on diverse datasets and offers live, interactive editing sessions. The technique possesses flexibility, precision, and generality to interactively modify the shape, position, and layout of generated objects. It allows users to control any number of handle and target points on the image, making it applicable to different object categories. The paper discusses a technique for interactive point-based manipulation of GAN-generated images using StyleGAN2. The proposed approach allows fine-grained control over spatial attributes using point-based editing and enables local semantics transferring between different samples. The method outperforms state-of-the-art point tracking approaches and handles long-range tracking better than previous methods. The user can set handle and target points to edit the image in an iterative optimization process. The approach achieves natural and superior results on various datasets. The article presents a method for interactive point-based manipulation of GAN-generated images using GAN inversion and a new point tracking approach. The method is evaluated quantitatively and qualitatively with comparisons to other methods, showing accurate tracking of handle points and achieving diverse and natural manipulation effects. The approach outperforms other methods in terms of tracking accuracy and image quality, as indicated by the FID score, and is robust under different numbers of handle points. The method allows masking the movable region and has extrapolation capability for creating images out of the training image distribution. Privacy regulations must be adhered to and texture-rich handle points are suggested. A binary mask can be used to reduce ambiguity in the manipulation. The approach largely pushes the upper bound of the task while maintaining a comfortable running time for users. This document provides a list of papers related to generative adversarial networks (GANs) and image synthesis, covering topics such as semantic image synthesis, 3D-aware image synthesis, and high-precision semantic image editing. The document references DragGAN, an interactive approach for point-based manipulation of images using pre-trained GAN models. The approach uses two novel ingredients: an optimization of latent codes that incrementally moves multiple handle points towards their target locations, and a point tracking procedure to faithfully trace the trajectory of the handle points. The method is general and does not rely on domain-specific modeling or auxiliary networks. Future work includes extending point-based editing to 3D generative models. A new method for interactive point-based manipulation using GANs is discussed in the paper. It involves optimizing the latent code to achieve manipulations while preserving image distribution, and W space allows for out-of-distribution manipulations. Feature blending can be used for ground preservation, but there are limitations such as distortion artifacts and texture-less regions. The method works well for dense keypoint cases, and the paper includes a qualitative comparison and real image manipulation examples. The authors reference previous work in GANs and image synthesis.
1741 word summary
The paper discusses a method for interactive point-based manipulation on the generative image manifold using GANs. The method involves optimizing the latent code to achieve manipulations while preserving image distribution. The use of W space allows for out-of-distribution manipulations. Feature blending can be used for ground preservation. Limitations include distortion artifacts and texture-less regions. The method works well for dense keypoint cases. The paper includes a qualitative comparison and real image manipulation examples. The authors reference previous work in GANs and image synthesis. This document provides references to various papers and research related to generative adversarial networks (GANs) and image manipulation. The papers cover topics such as 3D control over portrait images, disentangled geometry and appearance from monocular images, and learning a 3D generative model with flow. Other papers discuss optical flow, dense point trajectories, and score-based generative modeling. Additionally, there are papers on closed-form factorization of latent semantics, controllable face image generation, and image synthesis for multiple domains. The document also references papers on periodic implicit generative adversarial networks for 3D-aware image synthesis and efficient geometry-aware 3D generative adversarial networks. DragGAN is an interactive approach for intuitive point-based manipulation of images using pre-trained GAN models. The approach outperforms state-of-the-art methods by yielding pixel-precise image deformations and interactive performance. It leverages a pre-trained GAN to synthesize images that precisely follow user input, and also stay on the manifold of realistic images. The method uses two novel ingredients: an optimization of latent codes that incrementally moves multiple handle points towards their target locations, and a point tracking procedure to faithfully trace the trajectory of the handle points. The method is general and does not rely on domain-specific modeling or auxiliary networks. In future work, the authors plan to extend point-based editing to 3D generative models. This is a list of various papers related to generative adversarial networks (GANs) and image synthesis. The papers cover topics such as spatially-adaptive normalization, semantic image synthesis, 3D-aware image synthesis, high-precision semantic image editing, portrait rendering, free-view editable manipulation, conditional adversarial networks, optical flow estimation, denoising diffusion probabilistic networks, particle video manipulation, interpretable GAN controls, and more. The authors of these papers include Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu, Xingang Pan, Xudong Xu, Chen Change Loy, Christian Theobalt, Bo Dai, Ron Mokady, Omer Tov, Michal Yarom, Oran Lang, Inbar Mosseri, Tali Dekel, Daniel Fidler, Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler, Thomas Leimkuhler, George Drettakis, Diederik P Kingma, Jimmy Ba, Davis E. King, Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Erik Harkonen, Aaron Hertzmann, Sylvain Paris, Jiatao Gu, Lingjie Liu, Peng Wang, Ozair, Aaron Courville, Yoshua Bengio, Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Blobgan, Partha Ghosh, Pravir Singh Gupta, Roy Uziel, Anurag Ranjan, Michael J Black, Wayne Wu, Ziwei Liu, Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, and Chen Qian. The Drag Your GAN Interactive Point-based Manipulation method allows users to edit StyleGAN images by manipulating point-based handles. The approach must adhere to privacy regulations and avoid misusing the method to create images of real people with fake poses, expressions, or shapes. Texture-rich handle points are suggested as they suffer from less drift in tracking than texture-less regions. The editing approach outperforms all baselines in different object categories and has some extrapolation capability. A binary mask can be used to denote movable regions and reduce ambiguity in the manipulation. The approach largely pushes the upper bound of the task while maintaining a comfortable running time for users. The paper presents a method for interactive point-based manipulation of generative adversarial network (GAN) images. The proposed approach outperforms other methods in terms of tracking accuracy and image quality, as indicated by the FID score. The method is robust under different numbers of handle points and can move landmarks to target positions. The evaluation is performed under three settings with different numbers of landmarks, and the results are averaged over 1000 tests. The paper also presents an ablation study to investigate the effects of which feature to use in motion supervision and point tracking. The proposed method allows masking the movable region and has extrapolation capability for creating images out of the training image distribution. Quantitative evaluation on paired image reconstruction is also provided. The article presents a method for point-based manipulation of images using GAN. The method is evaluated quantitatively and qualitatively with comparisons to other methods, including UserControllableLT. The results show that the proposed approach accurately tracks handle points and achieves diverse and natural manipulation effects. Real image editing is also possible using GAN inversion techniques. The article includes tables and figures to demonstrate the effectiveness of the proposed method. This document presents a method for interactive point-based manipulation of GAN-generated images, allowing users to edit features such as pose, hair, shape, and expression. The approach uses GAN inversion to map real images to the latent space of StyleGAN, and a new point tracking approach is introduced to update handle points during interactive editing. The method is evaluated on several datasets and shown to be computationally efficient, with interactive editing requiring no more than 5 handle points and 2 otherwise. Implementation details are provided using PyTorch and the Adam optimizer. Overall, the method allows for easy and intuitive manipulation of GAN-generated images in real time. The paper proposes a method for interactive point-based manipulation on the generative image manifold using StyleGAN2. The method uses only six layers to affect attributes of the image for better editability. The spatial distribution manipulations depend on whether the user wants a more constrained image manifold or not. The approach achieves natural and superior results on various datasets. The paper also proposes a motion supervision loss that can be optimized either in the W space or in the W+ space. The loss is used to optimize the latent code for one step. The method performs point tracking on the feature maps of the generator using a shifted patch loss on the nearest neighbor search. The article discusses an interactive point-based manipulation technique for GAN-generated images. The user can set handle and target points to edit the image in an iterative optimization process. A motion supervision step drives the handle points towards the target points, and a point tracking step updates their positions. The user can also input a binary mask to denote the movable region during editing. The process continues until the handle points reach their corresponding target points. The technique involves a classic problem of estimating motion fields between two images and allows for semantic positions of handle points to track objects in the image. The pipeline involves motion supervision and point tracking steps and varies for different objects. An overview of the process is shown in Fig. 2. GANs are efficient for image synthesis but current diffusion models are slow and limited to high-level semantic editing. Natural language does not enable fine-grained control over images. StyleGAN2 architecture provides limited control over global pose or lighting. This work aims to develop an interactive image manipulation method for GANs where users only need to click on the images to define pairs of handle points and target points for interactive editing. The method outperforms the state-of-the-art point tracking approaches and handles long-range tracking better than previous approaches. The technique is called PIPs, which considers information across multiple frames and gives rise to a new point tracking method. The paper discusses various methods for point-based editing of generative models, including deep learning-based approaches and conventional optimization-based methods. While conventional methods are imprecise, deep learning-based methods have dominated the field in recent years. However, most deep learning-based approaches only support editing using a single point and do not enable controls such as changing the 3D pose of the object or handling multiple-point editing. The proposed approach allows fine-grained control over spatial attributes using point-based editing and enables users to perform local semantics transferring between different samples. Several methods have been proposed for editing unconditional GANs by manipulating the distribution of images and segmentation maps, and then computing new images corresponding to edited segmentation maps. Conditional GAN models like StyleGAN do not directly enable controllable editing of the generated images. The article discusses interactive point-based manipulation of generative adversarial networks (GANs) for high-resolution photorealistic image generation. The technique involves tracking handle points and supervising them to move towards target points, allowing for diverse and accurate image manipulation. The approach should possess flexibility, precision, and generality to interactively modify the shape, position, and layout of generated objects. Previous approaches lacked one or more of these properties, but this technique targets to achieve them all. It allows users to control any number of handle and target points on the image, making it applicable to different object categories. DragGAN is a tool that allows users to manipulate visual content by adjusting position, shape, expression, and body pose. It is based on generative adversarial networks (GANs) and combines with GAN inversion techniques to provide controllability over the synthesis of random photorealistic images. DragGAN also includes a GAN-based point tracking algorithm that outperforms existing point tracking approaches. The tool effectively moves user-defined handle points to achieve diverse manipulation effects across many object categories. It can hallucinate occluded content and deform following object rigidity. DragGAN does not rely on additional networks and achieves efficient manipulation in a few seconds on a single RTX 3090 GPU. It provides both motion supervision and precise point tracking through a shifted feature patch loss that optimizes the latent code. The tool is evaluated on diverse datasets including animals, humans, cars, and landscapes and offers live, interactive editing sessions. DragGAN is a new approach to interactive image manipulation and point tracking. It allows users to manipulate real images through GAN inversion and control the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. The approach consists of two main components: a feature-based motion supervision that drives handle points to move towards target positions, and a new point tracking approach that leverages discriminative generator features to keep localizing the position of the points in a user-interactive manner. DragGAN enables control of many spatial attributes like pose, shape, expression, and layout across diverse object categories. Users only need to click a few handle points (red) and target points (blue) on the image, and the approach will move the handle points to precisely reach their corresponding target points.