Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner

• URL: http://arxiv.org/abs/2407.18656v1
• Date: Fri, 26 Jul 2024 10:45:57 GMT
• Title: Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner
• Authors: Pengxiang Cai, Zhiwei Liu, Guibo Zhu, Yunfang Niu, Jinqiao Wang,
• Abstract summary: This paper employs a regression-based network to learn the variation patterns of StyleGAN latent codes during the image dragging process. We show that our method achieves state-of-the-art (SOTA) inference speed and image editing performance at the pixel-level granularity.
• Score: 28.939227214483953
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pixel-level fine-grained image editing remains an open challenge. Previous works fail to achieve an ideal trade-off between control granularity and inference speed. They either fail to achieve pixel-level fine-grained control, or their inference speed requires optimization. To address this, this paper for the first time employs a regression-based network to learn the variation patterns of StyleGAN latent codes during the image dragging process. This method enables pixel-level precision in dragging editing with little time cost. Users can specify handle points and their corresponding target points on any GAN-generated images, and our method will move each handle point to its corresponding target point. Through experimental analysis, we discover that a short movement distance from handle points to target points yields a high-fidelity edited image, as the model only needs to predict the movement of a small portion of pixels. To achieve this, we decompose the entire movement process into multiple sub-processes. Specifically, we develop a transformer encoder-decoder based network named 'Latent Predictor' to predict the latent code motion trajectories from handle points to target points in an autoregressive manner. Moreover, to enhance the prediction stability, we introduce a component named 'Latent Regularizer', aimed at constraining the latent code motion within the distribution of natural images. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) inference speed and image editing performance at the pixel-level granularity.

Related papers

• AdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing [14.543341303789445]
  We propose a novel mask-free point-based image editing method, AdaptiveDrag, which generates images that better align with user intent. To ensure a comprehensive connection between the input image and the drag process, we have developed a semantic-driven optimization. Building on these effective designs, our method delivers superior generation results using only the single input image and the handle-target point pairs.
  arXiv  Detail & Related papers  (2024-10-16T15:59:02Z)
• FastDrag: Manipulate Anything in One Step [20.494157877241665]
  We introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods.
  arXiv  Detail & Related papers  (2024-05-24T17:59:26Z)
• LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos [101.59710862476041]
  We present LightningDrag, a rapid approach enabling high quality drag-based image editing in 1 second. Unlike most previous methods, we redefine drag-based editing as a conditional generation task. Our approach can significantly outperform previous methods in terms of accuracy and consistency.
  arXiv  Detail & Related papers  (2024-05-22T15:14:00Z)
• Continuous Piecewise-Affine Based Motion Model for Image Animation [45.55812811136834]
  Image animation aims to bring static images to life according to driving videos. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. We propose to model motion from the source image to the driving frame in highly-expressive diffeo spaces.
  arXiv  Detail & Related papers  (2024-01-17T11:40:05Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• In-Domain GAN Inversion for Faithful Reconstruction and Editability [132.68255553099834]
  We propose in-domain GAN inversion, which consists of a domain-guided domain-regularized and a encoder to regularize the inverted code in the native latent space of the pre-trained GAN model. We make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property.
  arXiv  Detail & Related papers  (2023-09-25T08:42:06Z)
• DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing [94.24479528298252]
  DragGAN is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images. We present a challenging benchmark dataset called DragBench to evaluate the performance of interactive point-based image editing methods.
  arXiv  Detail & Related papers  (2023-06-26T06:04:09Z)
• Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold [79.94300820221996]
  DragGAN is a new way of controlling generative adversarial networks (GANs) DragGAN allows anyone to 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. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking.
  arXiv  Detail & Related papers  (2023-05-18T13:41:25Z)
• DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation [56.514462874501675]
  We propose a dynamic sparse attention based Transformer model to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details.
  arXiv  Detail & Related papers  (2022-07-13T11:12:03Z)
• Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network [0.0]
  In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions.
  arXiv  Detail & Related papers  (2020-02-04T09:10:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.