Reflection Generation for Composite Image Using Diffusion Model

• URL: http://arxiv.org/abs/2604.02168v1
• Date: Thu, 02 Apr 2026 15:35:28 GMT
• Title: Reflection Generation for Composite Image Using Diffusion Model
• Abstract summary: Shadow generation has been extensively studied, but reflection generation remains largely underexplored.<n>We inject the prior information of reflection placement and reflection appearance into foundation diffusion model.<n>To support training, we construct the first large-scale object reflection dataset DEROBA.
• Score: 22.699034413532285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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