mmPred: Radar-based Human Motion Prediction in the Dark

• URL: http://arxiv.org/abs/2512.00345v1
• Date: Sat, 29 Nov 2025 06:26:55 GMT
• Title: mmPred: Radar-based Human Motion Prediction in the Dark
• Authors: Junqiao Fan, Haocong Rao, Jiarui Zhang, Jianfei Yang, Lihua Xie,
• Abstract summary: Existing Human Motion Prediction methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns.<n>This work introduces radar as a novel sensing modality for HMP, for the first time.<n>We propose mmPred, the first diffusion-based framework tailored for radar-based HMP.
• Score: 43.00006337997152
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing Human Motion Prediction (HMP) methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns, limiting their real-world applications such as firefighting and healthcare. Motivated by the robustness and privacy-preserving nature of millimeter-wave (mmWave) radar, this work introduces radar as a novel sensing modality for HMP, for the first time. Nevertheless, radar signals often suffer from specular reflections and multipath effects, resulting in noisy and temporally inconsistent measurements, such as body-part miss-detection. To address these radar-specific artifacts, we propose mmPred, the first diffusion-based framework tailored for radar-based HMP. mmPred introduces a dual-domain historical motion representation to guide the generation process, combining a Time-domain Pose Refinement (TPR) branch for learning fine-grained details and a Frequency-domain Dominant Motion (FDM) branch for capturing global motion trends and suppressing frame-level inconsistency. Furthermore, we design a Global Skeleton-relational Transformer (GST) as the diffusion backbone to model global inter-joint cooperation, enabling corrupted joints to dynamically aggregate information from others. Extensive experiments show that mmPred achieves state-of-the-art performance, outperforming existing methods by 8.6% on mmBody and 22% on mm-Fi.

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