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RD-Diff: RLTransformer -Based Diffusion Model with Diversity-Inducing Modulator for Human Motion Prediction

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Computer Vision – ACCV 2024 (ACCV 2024)

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Abstract

Human Motion Prediction (HMP) is crucial for human-robot collaboration, surveillance, and autonomous driving applications. Recently, diffusion models have shown promising progress due to their ease of training and realistic generation capabilities. To enhance both accuracy and diversity of the diffusion model in HMP, we present RD-Diff : RLTransformer -based Diffusion model with Diversity-inducing modulator. First, to improve transformer’s effectiveness on the frequency representation of human motion transformed by Discrete Cosine Transform (DCT), we introduce a novel Regulated Linear Transformer (RLTransformer ) with a specially designed linear-attention mechanism. Next, to further enhance the performance, we propose a Diversity-Inducing Modulator (DIM ) to generate noise-modulated observation conditions for a pretrained diffusion model. Experimental results show that our RD-Diff establishes a new state-of-the-art performance on both accuracy and diversity compared to existing methods.

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Authors and Affiliations

  1. Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore

    Haosong Zhang, Mei Chee Leong & Liyuan Li

  2. Nanyang Technological University, Singapore, Singapore

    Haosong Zhang & Weisi Lin

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  1. Haosong Zhang
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  2. Mei Chee Leong
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  3. Liyuan Li
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  4. Weisi Lin
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Correspondence to Haosong Zhang .

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  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Zhang, H., Leong, M.C., Li, L., Lin, W. (2025). RD-Diff: RLTransformer -Based Diffusion Model with Diversity-Inducing Modulator for Human Motion Prediction. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15472. Springer, Singapore. https://doi.org/10.1007/978-981-96-0885-0_12

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  • DOI: https://doi.org/10.1007/978-981-96-0885-0_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0884-3

  • Online ISBN: 978-981-96-0885-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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