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  3. Drivegen: Shared Video-condition Encoding For Autonomous Multi-view Video Generation.
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  3. Drivegen: Shared Video-condition Encoding For Autonomous Multi-view Video Generation.

Related Experiment Videos

DriveGen: Shared Video-Condition Encoding for Autonomous Multi-View Video Generation.

Yuhao Kang, Haolin Li, Sanyuan Zhao

    IEEE Transactions on Visualization and Computer Graphics
    |June 2, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    DriveGen enhances autonomous driving by generating realistic corner-case videos using multi-view data. This method improves consistency and reduces computational costs for better data expansion.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Autonomous Systems

    Background:

    • Autonomous driving faces challenges with corner cases like severe weather and poor lighting.
    • Collecting and annotating large datasets for these scenarios is expensive and time-consuming.
    • Generative models offer a solution by expanding existing corner-case data.

    Purpose of the Study:

    • To develop a novel generative model for creating high-quality, controlled autonomous driving videos, specifically addressing challenges with multi-view data.
    • To improve spatiotemporal consistency and annotation alignment in generated videos.
    • To reduce the computational cost associated with generating diverse driving scenarios.

    Main Methods:

    • Proposed DriveGen, a model utilizing 4D position embeddings for multi-view video data.
  • Implemented Dual-Scale Full Attention for global and local spatiotemporal consistency.
  • Introduced a Shared Video-Condition Encoding (SVCE) Mechanism with a 3D VAE for efficient annotation encoding and pixel-level alignment.

    • Main Results:

    • DriveGen achieved state-of-the-art performance in generating controlled autonomous driving videos.
    • The model demonstrated improved global and local spatiotemporal consistency compared to existing methods.
    • Achieved pixel-level alignment with significantly fewer learnable parameters (0.37M).

    Conclusions:

    • DriveGen effectively addresses the limitations of existing methods in generating multi-view autonomous driving videos.
    • The proposed approach enhances generation quality, consistency, and computational efficiency.
    • DriveGen shows significant promise for expanding corner-case datasets and advancing autonomous driving technology.