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arxiv: 2408.07605 · v1 · pith:KMHO7OHJ · submitted 2024-08-14 · cs.CV

Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving

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classification cs.CV
keywords panaceadrivingvideoautonomousdatadetectionframeworkgeneration
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The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework for generating video data in driving scenes. Built upon the foundation of our previous work, Panacea, Panacea+ adopts a multi-view appearance noise prior mechanism and a super-resolution module for enhanced consistency and increased resolution. Extensive experiments show that the generated video samples from Panacea+ greatly benefit a wide range of tasks on different datasets, including 3D object tracking, 3D object detection, and lane detection tasks on the nuScenes and Argoverse 2 dataset. These results strongly prove Panacea+ to be a valuable data generation framework for autonomous driving.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Point as Skeleton: Accumulated Point Cloud Enhanced Autoregressive Generation for Closed-Loop Autonomous Driving Simulation

    cs.CV 2026-07 conditional novelty 6.0

    Point-cloud skeleton conditions and a Reset-and-Roll inference scheme enable stable frame-wise autoregressive driving video generation for closed-loop autonomous driving simulation.

  2. DriveCtrl: Conditioned Sim-to-Real Driving Video Generation

    cs.CV 2026-05 unverdicted novelty 5.0

    DriveCtrl is a depth-conditioned controllable framework that generates realistic driving videos from simulation while preserving annotations and scene dynamics.