GA-Drive: Geometry-Appearance Decoupled Modeling for Free-viewpoint Driving Scene Generatio

• URL: http://arxiv.org/abs/2602.20673v1
• Date: Tue, 24 Feb 2026 08:22:42 GMT
• Title: GA-Drive: Geometry-Appearance Decoupled Modeling for Free-viewpoint Driving Scene Generatio
• Authors: Hao Zhang, Lue Fan, Qitai Wang, Wenbo Li, Zehuan Wu, Lewei Lu, Zhaoxiang Zhang, Hongsheng Li,
• Abstract summary: We present GA-Drive, a novel simulation framework capable of generating camera views along user-specified novel trajectories.<n>GA-Drive synthesizes novel pseudo-views using geometry information.<n>These pseudo-views are then transformed into photorealistic views using a trained video diffusion model.
• Score: 62.07995406671134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A free-viewpoint, editable, and high-fidelity driving simulator is crucial for training and evaluating end-to-end autonomous driving systems. In this paper, we present GA-Drive, a novel simulation framework capable of generating camera views along user-specified novel trajectories through Geometry-Appearance Decoupling and Diffusion-Based Generation. Given a set of images captured along a recorded trajectory and the corresponding scene geometry, GA-Drive synthesizes novel pseudo-views using geometry information. These pseudo-views are then transformed into photorealistic views using a trained video diffusion model. In this way, we decouple the geometry and appearance of scenes. An advantage of such decoupling is its support for appearance editing via state-of-the-art video-to-video editing techniques, while preserving the underlying geometry, enabling consistent edits across both original and novel trajectories. Extensive experiments demonstrate that GA-Drive substantially outperforms existing methods in terms of NTA-IoU, NTL-IoU, and FID scores.

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