ReCamDriving: LiDAR-Free Camera-Controlled Video Synthesis for Novel Trajectories

Synthesizing multi-pass videos is important for autonomousdriving. While current repair-based methods often struggle with out-of-distribution artifacts, camera-controlled methods often produce 3D-inconsistent results due to sparse LiDAR cues. We propose ReCamDriv-ing, a purely vision-based framework that achieves camera-controlledgeneration by leveraging dense, structurally complete 3DGS renderingsas geometric guidance. Specifically, to prevent the model from overfit-ting to a trivial repair solution when conditioning on 3DGS render-ings, we adopt a two-stage progressive training paradigm: the first stageuses camera poses for coarse control, while the second stage incorpo-rates 3DGS renderings for fine-grained viewpoint and geometric guid-ance. Furthermore, to align training and inference camera transforma-tion patterns, we propose a 3DGS-based cross-trajectory data curationstrategy, enabling consistent lateral-trajectory supervision from single-pass videos. Based on this strategy, we construct the ParaDrive dataset,containing approximately 110K parallel-trajectory video pairs. Exten-sive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency. Project website:https://recamdriving.github.io/.