用扩散模型生成逼真反射，提升图像合成真实感。
Reflection Generation for Composite Image Using Diffusion Model
- 将反射位置与外观先验注入基础扩散模型
- 构建首个大规模反射数据集DEROBA支持训练
- 区分两类反射，设计类型感知模型，效果更真实
图像合成需将前景物体融入背景并生成一致的环境效果，如阴影和反射。尽管阴影生成已广泛研究，反射生成仍鲜有探索。本文聚焦反射生成，将反射位置与外观的先验信息注入基础扩散模型，并将反射分为两类，采用类型感知的模型设计。为支持训练，构建了首个大规模物体反射数据集DEROBA。实验表明，该方法生成的反射在物理一致性与视觉真实性上表现优异，建立了反射生成的新基准。
原文摘要 · Abstract (English)
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.
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