用场景几何约束生成镜面反射，让镜子内容更真实。
Fill My Mirror: Geometry-Constrained Mirror Inpainting
- 基于可见场景几何投影，预估镜面内容位置
- 双掩码扩散模型减少投影伪影，提升反射一致性
- 无需训练，适用于复杂真实场景
镜子在真实图像中很常见，但生成几何一致的镜面反射仍具挑战。与一般物体不同，镜面外观依赖于场景几何和视角，仅靠学习到的外观先验难以合成。本文在镜面修复任务中解决此问题：场景固定，仅生成镜面区域。核心思想是镜面内容大多由可见场景几何决定，无需凭空臆造。我们估计场景几何，并将可见内容投影至镜面以恢复由几何决定的反射区域。随后，通过双掩码扩散策略，结合几何约束与模型先验，完成镜面区域生成，有效减少投影伪影，提升反射一致性。方法无需训练，可应用于复杂真实场景。我们在 MirrorBench-V2（合成数据）和真实图像上评估，使用标准与几何感知指标，结果表明显式利用场景几何能显著提升一致性。
原文摘要 · Abstract (English)
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.
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