Reflection Generation for Composite Image Using Diffusion Model
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917380572053504 |
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| author | Zhao, Haonan Liu, Qingyang Chen, Jiaxuan Niu, Li |
| author_facet | Zhao, Haonan Liu, Qingyang Chen, Jiaxuan Niu, Li |
| contents | Image composition involves inserting a foreground object into the background while synthesizing environment-consistent effects such as shadows and reflections. Although shadow generation has been extensively studied, reflection generation remains largely underexplored. In this work, we focus on reflection generation. We inject the prior information of reflection placement and reflection appearance into foundation diffusion model. We also divide reflections into two types and adopt type-aware model design. To support training, we construct the first large-scale object reflection dataset DEROBA. Experiments demonstrate that our method generates reflections that are physically coherent and visually realistic, establishing a new benchmark for reflection generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02168 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reflection Generation for Composite Image Using Diffusion Model Zhao, Haonan Liu, Qingyang Chen, Jiaxuan Niu, Li Computer Vision and Pattern Recognition Image composition involves inserting a foreground object into the background while synthesizing environment-consistent effects such as shadows and reflections. Although shadow generation has been extensively studied, reflection generation remains largely underexplored. In this work, we focus on reflection generation. We inject the prior information of reflection placement and reflection appearance into foundation diffusion model. We also divide reflections into two types and adopt type-aware model design. To support training, we construct the first large-scale object reflection dataset DEROBA. Experiments demonstrate that our method generates reflections that are physically coherent and visually realistic, establishing a new benchmark for reflection generation. |
| title | Reflection Generation for Composite Image Using Diffusion Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.02168 |