Group Editing: Edit Multiple Images in One Go

• URL: http://arxiv.org/abs/2603.22883v3
• Date: Thu, 26 Mar 2026 10:38:34 GMT
• Title: Group Editing: Edit Multiple Images in One Go
• Abstract summary: GroupEditing is a framework that builds explicit and implicit relationships among images within a group.<n>GroupEditData is a dataset containing high-quality masks and detailed captions for numerous image groups.<n>We present GroupEditBench, a benchmark designed to evaluate the effectiveness of group-level image editing.
• Score: 48.78947366708772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we tackle the problem of performing consistent and unified modifications across a set of related images. This task is particularly challenging because these images may vary significantly in pose, viewpoint, and spatial layout. Achieving coherent edits requires establishing reliable correspondences across the images, so that modifications can be applied accurately to semantically aligned regions. To address this, we propose GroupEditing, a novel framework that builds both explicit and implicit relationships among images within a group. On the explicit side, we extract geometric correspondences using VGGT, which provides spatial alignment based on visual features. On the implicit side, we reformulate the image group as a pseudo-video and leverage the temporal coherence priors learned by pre-trained video models to capture latent relationships. To effectively fuse these two types of correspondences, we inject the explicit geometric cues from VGGT into the video model through a novel fusion mechanism. To support large-scale training, we construct GroupEditData, a new dataset containing high-quality masks and detailed captions for numerous image groups. Furthermore, to ensure identity preservation during editing, we introduce an alignment-enhanced RoPE module, which improves the model's ability to maintain consistent appearance across multiple images. Finally, we present GroupEditBench, a dedicated benchmark designed to evaluate the effectiveness of group-level image editing. Extensive experiments demonstrate that GroupEditing significantly outperforms existing methods in terms of visual quality, cross-view consistency, and semantic alignment.

Related papers

• Rethinking Structure Preservation in Text-Guided Image Editing with Visual Autoregressive Models [18.626758540572894]
  We propose a novel text-guided image editing framework rooted in an analysis of intermediate feature distributions within visual autoregressive ( VAR) models.<n>First, we introduce a coarse-to-fine token localization strategy that can refine editable regions, balancing editing fidelity and background preservation.<n>Second, we analyze the intermediate representations of VAR models and identify structure-related features, by which we design a simple yet effective feature injection mechanism.<n>Third, we develop a reinforcement learning-based adaptive feature injection scheme that automatically learns scale- and layer-specific injection ratios to jointly optimize editing fidelity and structure preservation.
  arXiv  Detail & Related papers  (2026-03-30T12:35:33Z)
• EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing [84.7089707244905]
  Masked Generative Transformers (MGTs) exhibit a localized decoding paradigm that endows them with the inherent capacity to preserve non-relevant regions during the editing process.<n>We introduce the first MGT-based image editing framework, termed EditMGT.<n>We demonstrate that EditMGT's cross-attention maps provide informative localization signals for localizing edit-relevant regions.<n>We also introduce region-hold sampling, which restricts token flipping within low-attention areas to suppress spurious edits.
  arXiv  Detail & Related papers  (2025-12-12T16:51:19Z)
• Image Editing As Programs with Diffusion Models [69.05164729625052]
  We introduce Image Editing As Programs (IEAP), a unified image editing framework built upon the Diffusion Transformer (DiT) architecture.<n>IEAP approaches instructional editing through a reductionist lens, decomposing complex editing instructions into sequences of atomic operations.<n>Our framework delivers superior accuracy and semantic fidelity, particularly for complex, multi-step instructions.
  arXiv  Detail & Related papers  (2025-06-04T16:57:24Z)
• Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions [20.617718631292696]
  We develop a novel paradigm for instruction-driven image editing that leverages widely available and enormous text-image pairs.<n>Our approach introduces a multi-scale learnable region to localize and guide the editing process.<n>By treating the alignment between images and their textual descriptions as supervision and learning to generate task-specific editing regions, our method achieves high-fidelity, precise, and instruction-consistent image editing.
  arXiv  Detail & Related papers  (2025-05-25T22:40:59Z)
• ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models [11.830273909934688]
  Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality images. We propose ReEdit, a modular and efficient end-to-end framework that captures edits in both text and image modalities. Our results demonstrate that ReEdit consistently outperforms contemporary approaches both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2024-11-06T15:19:24Z)
• Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing [60.730661748555214]
  We introduce textbfTask-textbfOriented textbfDiffusion textbfInversion (textbfTODInv), a novel framework that inverts and edits real images tailored to specific editing tasks. ToDInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability.
  arXiv  Detail & Related papers  (2024-08-23T22:16:34Z)
• Consolidating Attention Features for Multi-view Image Editing [126.19731971010475]
  We focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views. We introduce QNeRF, a neural radiance field trained on the internal query features of the edited images. We refine the process through a progressive, iterative method that better consolidates queries across the diffusion timesteps.
  arXiv  Detail & Related papers  (2024-02-22T18:50:18Z)
• iEdit: Localised Text-guided Image Editing with Weak Supervision [53.082196061014734]
  We propose a novel learning method for text-guided image editing. It generates images conditioned on a source image and a textual edit prompt. It shows favourable results against its counterparts in terms of image fidelity, CLIP alignment score and qualitatively for editing both generated and real images.
  arXiv  Detail & Related papers  (2023-05-10T07:39:14Z)
• EditGAN: High-Precision Semantic Image Editing [120.49401527771067]
  EditGAN is a novel method for high quality, high precision semantic image editing. We show that EditGAN can manipulate images with an unprecedented level of detail and freedom. We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data.
  arXiv  Detail & Related papers  (2021-11-04T22:36:33Z)

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.