ReCamDriving: LiDAR-Free Camera-Controlled Novel Trajectory Video Generation
We propose ReCamDriving, a purely vision-based, camera-controlled novel-trajectory video generation framework. While repair-based methods fail to restore complex artifacts and LiDAR-based approaches rely on sparse and incomplete cues, ReCamDriving leverages dense and scene-complete 3DGS renderings for explicit geometric guidance, achieving precise camera-controllable generation. To mitigate overfitting to restoration behaviors when conditioned on 3DGS renderings, ReCamDriving adopts a two-stage training paradigm: the first stage uses camera poses for coarse control, while the second stage incorporates 3DGS renderings for fine-grained viewpoint and geometric guidance. Furthermore, we present a 3DGS-based cross-trajectory data curation strategy to eliminate the train-test gap in camera transformation patterns, enabling scalable multi-trajectory supervision from monocular videos. Based on this strategy, we construct the ParaDrive dataset, containing over 110K parallel-trajectory video pairs. Extensive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency.
6932cf153962c926cf6803be
Yaokun Li
claimed_verified
2025-12-05T15:14:30.924Z
6649afbb32236a0fad74434e
/avatars/b0b707e4f0f48ff274648ca3c1f35c06.svg
Yaokun Li
Iron-lyk
user
Iron-lyk
6932cf153962c926cf6803bf
Shuaixian Wang
admin_assigned
2025-12-09T11:49:42.267Z
6937e4d1015eea3fe55fae22
/avatars/ee75ce2e869e6784f98b599be5e5fc61.svg
Shuaixian Wang
wangshx
user
wangshx
6932cf153962c926cf6803c0
Mantang Guo
6932cf153962c926cf6803c1
Jiehui Huang
6932cf153962c926cf6803c2
Taojun Ding
6932cf153962c926cf6803c3
Mu Hu
6932cf153962c926cf6803c4
Kaixuan Wang
6932cf153962c926cf6803c5
Shaojie Shen
6932cf153962c926cf6803c6
Guang Tan
ReCamDriving: LiDAR-Free Camera-Controlled Novel Trajectory Video Generation
We propose ReCamDriving, a purely vision-based, camera-controlled novel-trajectory video generation framework. While repair-based methods fail to restore complex artifacts and LiDAR-based approaches rely on sparse and incomplete cues, ReCamDriving leverages dense and scene-complete 3DGS renderings for explicit geometric guidance, achieving precise camera-controllable generation. To mitigate overfitting to restoration behaviors when conditioned on 3DGS renderings, ReCamDriving adopts a two-stage training paradigm: the first stage uses camera poses for coarse control, while the second stage incorporates 3DGS renderings for fine-grained viewpoint and geometric guidance. Furthermore, we present a 3DGS-based cross-trajectory data curation strategy to eliminate the train-test gap in camera transformation patterns, enabling scalable multi-trajectory supervision from monocular videos. Based on this strategy, we construct the ParaDrive dataset, containing over 110K parallel-trajectory video pairs. Extensive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency.
ReCamDriving generates camera-controlled novel-trajectory videos using dense 3DGS renderings and a two-stage training approach, achieving state-of-the-art results in controllability and consistency.
camera-controlled 3DGS renderings two-stage training camera poses cross-trajectory data curation ParaDrive dataset camera controllability structural consistency