Recognition of driver behavior in autonomous driving environments not only captures risky behaviors such as driver distraction in a timely manner, but also has great potential to improve human-vehicle communication and increase personalized comfort in driving. Diffusion modeling has revolutionized several application areas, but 3D pose estimation for drivers is still involved. In this paper, we propose a novel 3D driver action estimation method, named DiffDrive, which achieves more robust and accurate pose estimation in driving environments in the face of dark light and occlusion by effectively incorporating a multi-modal conditional prior in the diffusion process to guide the global structures. We evaluate the effectiveness of DiffDrive and its superiority over existing models using the Drive&Act dataset.


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    Titel :

    Multi-modal Guidance for 3D Driver Pose Estimation with Diffusion


    Beteiligte:
    Wang, Rui (Autor:in) / Yu, Xiaoyang (Autor:in) / Huang, Huihui (Autor:in) / Miao, Zhuang (Autor:in) / Ren, Chunxiao (Autor:in) / Jian, Muwei (Autor:in) / Yu, Hui (Autor:in)


    Erscheinungsdatum :

    08.11.2024


    Format / Umfang :

    590637 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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