发表机构
Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PhysReflect提出几何与感知引导的扩散框架，通过几何损失和感知损失直接监督镜面反射，在合成和真实基准上超越现有方法，提升反射的物理合理性。
AI 中文摘要
扩散模型能够生成高质量的图像，但常常违反控制镜面反射的物理规律。反射图像经常出现几何畸变，包括位置偏移、方向错位、比例失衡和结构扭曲。即使在当代最先进的生成系统中，这些缺陷依然明显。现有方法通过合成数据扩展或辅助深度条件化来缓解这一问题，但它们仅依赖潜在空间噪声重建损失作为隐式监督，无法直接强制执行反射特有的几何和感知约束。为弥补这一差距，我们提出了PhysReflect，一种几何与感知引导的扩散框架，它在每个训练步骤将预测的干净潜在表示解码到像素空间，并通过两个互补的可微分目标应用退火监督。几何损失通过稀疏对极对应和密集边界投影对齐来强制执行镜面诱导的空间一致性，其中基于SAM2的TwinTrack机制为边界感知监督提供稳定的镜内定位。感知损失通过结合语义一致性损失（利用DINOv2特征保持反射身份和外观）和光照一致性损失（在单目几何先验下正则化深度、表面法线和光照一致性）来保留反射外观。在合成和真实世界基准上的实验表明，PhysReflect在几何、感知和物理合理性指标以及定性视觉结果方面均优于先前的镜面反射方法。
英文摘要
Diffusion models generate high-quality images, yet often violate the physical laws governing mirror reflections. Reflections often suffer from geometric aberrations, including positional offsets, directional misalignment, proportional imbalance, and structural distortion. These failures remain evident even in contemporary state-of-the-art generative systems. Existing methods itigate this problem through synthetic data scaling or auxiliary depth conditioning, yet their merely reliance on latent-space noise reconstruction losses as implicit supervision prevents direct enforcement of reflection-specific geometric and perceptual constraints. To bridge this gap, we present PhysReflect, a geometry and perception guided diffusion framework that decodes the predicted clean latent into pixel space at each training step and applies annealed supervision through two complementary differentiable objectives. The Geometric Loss enforces mirror-induced spatial consistency through sparse epipolar correspondence and dense boundary projection alignment, where a SAM2-based TwinTrack mechanism provides stable in-mirror localization for boundary-aware supervision. The Perceptual Loss preserves reflected appearance by combining Semantic Consistency Loss, which maintains reflected identity and appearance via DINOv2 features, and Lighting Consistency Loss, which regularizes depth, surface-normal, and illumination coherence under monocular geometry priors. Experiments on synthetic and real-world benchmarks show that PhysReflect outperforms prior mirror-reflection methods in geometric, perceptual, and physical-plausibility metrics, as well as qualitative visual results.
