Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.27637 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908919502209024 |
|---|---|
| author | Lee, Sanghyeon Lee, Minwoo Shin, Euijin Kim, Kangyeol Choi, Seunghwan Choo, Jaegul |
| author_facet | Lee, Sanghyeon Lee, Minwoo Shin, Euijin Kim, Kangyeol Choi, Seunghwan Choo, Jaegul |
| contents | We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, panel-specific orthogonal operators onto the backbone's frozen positional encodings. This design provides two desirable properties: (1) isometry, which preserves the geometry of internal features, and (2) same-panel invariance, which maintains the model's pre-trained intra-panel synthesis behavior. Through controlled experiments, we demonstrate that the effectiveness of our adaptation method is not tied to a specific positional encoding design but generalizes across diverse positional encoding regimes. By enabling effective panel-relative conditioning, the proposed method consistently improves in-context image-based instructional editing pipelines, including state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27637 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation Lee, Sanghyeon Lee, Minwoo Shin, Euijin Kim, Kangyeol Choi, Seunghwan Choo, Jaegul Computer Vision and Pattern Recognition We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, panel-specific orthogonal operators onto the backbone's frozen positional encodings. This design provides two desirable properties: (1) isometry, which preserves the geometry of internal features, and (2) same-panel invariance, which maintains the model's pre-trained intra-panel synthesis behavior. Through controlled experiments, we demonstrate that the effectiveness of our adaptation method is not tied to a specific positional encoding design but generalizes across diverse positional encoding regimes. By enabling effective panel-relative conditioning, the proposed method consistently improves in-context image-based instructional editing pipelines, including state-of-the-art approaches. |
| title | OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.27637 |