Fill My Mirror: Geometry-Constrained Mirror Inpainting

2026-09-03

AI Summary

本文解决了生成几何一致的镜像反射难题，通过估计场景几何并将可见内容投影到镜子中，结合生成模型的两掩模扩散策略来完成镜子区域。

Abstract

Mirrors are common in real-world images, yet producing geometrically consistent reflections with generative models remains challenging. Unlike most objects, mirror appearance depends on scene geometry and viewpoint, making it hard to synthesize using learned appearance priors alone. We address this in the mirror inpainting setting, where the scene is fixed and only the mirror region is generated. Our key insight is that much mirror content is geometrically constrained by the visible scene and need not be hallucinated. We estimate scene geometry and project visible content into the mirror to recover reflection regions determined by geometry. A generative model then completes the mirror region via a two-mask diffusion strategy balancing geometric constraints with the model's learned priors, reducing projection artifacts and improving reflection consistency. The method is training-free and applicable to complex real-world scenes. We evaluate on MirrorBench-V2 (synthetic) and real images. Using standard and geometry-aware metrics, we show that explicitly using scene geometry improves consistency.

Problem

• Mirror Inpainting
• Geometric Consistency
• Scene Geometry

Innovation

• geometry-constrained
• mirror inpainting
• two-mask diffusion strategy
• scene geometry
• reflection consistency