Group Editing: Edit Multiple Images in One Go
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910075083292672 |
|---|---|
| author | Ma, Yue Wang, Xinyu Ma, Qianli Wang, Qinghe Zheng, Mingzhe Yang, Xiangpeng Li, Hao Zhao, Chongbo Ying, Jixuan Yang, Harry Liu, Hongyu Chen, Qifeng |
| author_facet | Ma, Yue Wang, Xinyu Ma, Qianli Wang, Qinghe Zheng, Mingzhe Yang, Xiangpeng Li, Hao Zhao, Chongbo Ying, Jixuan Yang, Harry Liu, Hongyu Chen, Qifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22883 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Group Editing: Edit Multiple Images in One Go Ma, Yue Wang, Xinyu Ma, Qianli Wang, Qinghe Zheng, Mingzhe Yang, Xiangpeng Li, Hao Zhao, Chongbo Ying, Jixuan Yang, Harry Liu, Hongyu Chen, Qifeng Computer Vision and Pattern Recognition 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. |
| title | Group Editing: Edit Multiple Images in One Go |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.22883 |