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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    Title :

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


    Contributors:
    Wang, Rui (author) / Yu, Xiaoyang (author) / Huang, Huihui (author) / Miao, Zhuang (author) / Ren, Chunxiao (author) / Jian, Muwei (author) / Yu, Hui (author)


    Publication date :

    2024-11-08


    Size :

    590637 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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