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.

翻译：图像合成涉及将前景对象插入背景中，同时生成与环境一致的阴影和倒影等效果。尽管阴影生成已被广泛研究，但倒影生成仍尚待深入探索。本文聚焦于倒影生成任务。我们将倒影位置与倒影外观的先验信息注入基础扩散模型中，并将倒影分为两类，采用类型感知的模型设计。为支持训练，我们构建了首个大规模物体倒影数据集DEROBA。实验表明，我们的方法能够生成物理连贯且视觉逼真的倒影，为倒影生成树立了新的基准。