Edit One for All: Interactive Batch Image Editing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910301319856128 |
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| author | Nguyen, Thao Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae |
| author_facet | Nguyen, Thao Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae |
| contents | In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner. However, most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process, this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g., make the face frontal), our method can automatically transfer that edit to other test images, so that regardless of their initial state (pose), they all arrive at the same final state (e.g., all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods, while having more visual consistency and saving significant time and human effort. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_10219 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Edit One for All: Interactive Batch Image Editing Nguyen, Thao Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae Computer Vision and Pattern Recognition In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner. However, most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process, this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g., make the face frontal), our method can automatically transfer that edit to other test images, so that regardless of their initial state (pose), they all arrive at the same final state (e.g., all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods, while having more visual consistency and saving significant time and human effort. |
| title | Edit One for All: Interactive Batch Image Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.10219 |