Reflection Generation for Composite Image Using Diffusion Model
This work addresses a largely underexplored issue in image composition for computer vision applications, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of generating physically coherent and visually realistic reflections for composite images, achieving a new benchmark for reflection generation.
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.