PhysReflect: Geometry and Perception Guided Diffusion for Physically-Plausible Mirror Reflections

2026-09-20

AI Summary

本文提出PhysReflect框架，通过几何和感知引导的扩散方法解决镜面反射中的物理不一致问题，使用几何损失和感知损失来优化生成图像。

Abstract

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.

Problem

• Diffusion Models
• Mirror Reflections
• Geometric Aberrations
• Physical Laws
• Perceptual Constraints

Innovation

• Geometry and Perception Guided Diffusion
• Geometric Loss
• Perceptual Loss
• Mirror Reflections