Inverse Rendering is the process of inferring a scene’s geometry, materials, and lighting from image data. It reverses the traditional rendering pipeline, using optimization or neural networks to estimate how light interacts with surfaces. Applications include augmented reality, robotics, and realistic digital content creation. Inverse rendering integrates computer vision, graphics, and physics-based modeling. Its results improve visual realism and environmental understanding in virtual and real-world settings.

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AutoScape: Geometry-Consistent Long-Horizon Scene Generation

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This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically consistent keyframes, serving as reliable anchors for the scenes appearance and geometry. To maintain long-range geometric consistency, the model 1) jointly handles image and depth in a shared latent space, 2) explicitly conditions on the existing scene geometry (i.e., rendered point clouds) from previously generated keyframes, and 3) steers the sampling process with a warp-consistent guidance. Given high-quality RGB-D keyframes, a video diffusion model then interpolates between them to produce dense nd coherent video frames. AutoScape generates realistic and geometrically consistent driving videos of over 20 seconds, improving the long-horizon FID and FVD scores over the prior state-of-the-art by 48.6% and 43.0%, respectively.

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