城市街景渲染：物理模型+AI，视频更真更可控
做城市街景的3D重建和渲染，过去两条路：物理模型算得准但画面有瑕疵，生成模型视频好看但没法精细控制。这篇把两者合起来——先用物理模型算出场景的材质、光照等属性，再用生成模型把渲染出的瑕疵修掉。结果是你既能像调灯光一样控制场景（比如换光源、加物体），又能得到像真实拍摄一样流畅的视频。它支持从新视角重新打光、模拟夜间效果、甚至往场景里插入动态物体。做自动驾驶仿真、数字城市、影视后期的人，这是你明天就能试的那种。
📄 原文摘要(英文)
Inverse rendering of urban scenes from captured videos enables numerous applications, including content creation and autonomous driving simulation. Physically-based rendering methods follow and control lighting physics, but suffer from reconstruction and rendering artifacts. While generative models produce realistic videos, they offer limited consistency and controllability. We present BRDFusion, a unified framework that combines two complementary models for inverse and forward rendering. Specifically, BRDFusion recovers explicit, consistent scene properties with physical modeling and alleviates optimization ambiguity with generative priors. During forward rendering, the physical model provides controllable rendering from the scene configuration, and the generative model denoises and fixes artifacts. Therefore, our method produces high-quality videos while allowing precise control, outperforming baselines in real and synthetic scenes. Moreover, BRDFusion supports novel-view relighting, night simulation, and dynamic object insertion/editing. Project page: https://shigon255.github.io/brdfusion-page/