To perform image editing based on single-view, inverse physically based rendering, we present a method combining a learning-based approach with progressive differentiable rendering. Given an image, our method leverages neural networks to predict initial material properties. Progressive differentiable rendering is then used to optimize the environment map and refine the material properties with the goal of closely matching the rendered result to the input image. We require only a single image while other inverse rendering methods based on the rendering equation require multiple views. In comparison to single-view methods that rely on neural renderers, our approach achieves more realistic light material interactions, accurate shadows, and global illumination. Furthermore, with optimized material properties and illumination, our method enables a variety of tasks, including physically based material editing, object insertion, and relighting. We also propose a method for material transparency editing that operates effectively without requiring full scene geometry. Compared with methods based on Stable Diffusion, our approach offers stronger interpretability and more realistic light refraction based on empirical results.

本研究解决了单视图下物理基础渲染的图像编辑问题，提出了一种结合学习方法与渐进可微渲染的新方法。该方法能够仅使用单张图片预测材料属性，并通过优化环境图和材料属性来更准确地还原输入图像，显著提升了光照与材料互动的真实感，具有广泛的应用潜力。

物质主义：基于物理的单图像反向渲染编辑