AutoScape: Geometry-Consistent Long-Horizon Scene Generation
On the map: Diffusion models → Diffusion and flow policies for robotics
Abstract (arXiv)
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 scene's 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 and 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 86 of its 91 references that are in the map. First posted on arXiv 23 Oct 2025.
Builds on
86 of 91 references.
Closest ancestors · 5
Same direction, newest first
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- 2024MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control21 Nov
- 2024Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion18 Jul
- 2023Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion2 Nov
- 2023MagicDrive: Street View Generation with Diverse 3D Geometry Control4 Oct
Other references · 81
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38 more
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50 most cited of 81 All 81 (JSON)
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Citing papers per year: 2026: 2.
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