DiFF：面向人体运动流的多普勒信息流匹配
DiFF: Doppler-informed Flow Matching for Human Motion Flow
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中文总结 AI 辅助
针对4D毫米波雷达点云稀疏噪声导致的人体非刚性运动流估计不适定问题，提出融合多普勒先验与KAN条件流匹配的生成框架DiFF，在真实数据集上达到SOTA，3D端点误差降至毫米级。
中文摘要 AI 辅助
通过隐私保护的4D毫米波（mmWave）雷达感知人体运动，对于下一代人机交互（HRI）至关重要，其中点云场景流作为基础的运动表示。然而，4D雷达点云的极度稀疏性和噪声使得非刚性运动流估计严重不适定——这一问题现有的以刚性为中心的方法和先前工作未能充分解决，主要原因在于它们忽略了4D雷达中固有的丰富多普勒速度线索。我们提出DiFF，一种生成式框架，将多普勒信息运动先验与基于Kolmogorov-Arnold网络（KAN）的条件流匹配模型相结合。其核心是，KAN注意力机制实现富有表现力的特征提取，而先验引导的生成过程利用多普勒线索来正则化不适定的解空间。大量实验表明，DiFF在多种真实世界数据集上达到了最先进的（SOTA）性能，在mmBody基准上将3D端点误差降低到毫米级别。
英文摘要
Perceiving human motion via privacy-preserving 4D millimeter-wave (mmWave) radar is critical for next-generation human-robot interaction (HRI), where point cloud scene flow serves as a foundational motion representation. Yet the extreme sparsity and noise of 4D radar point clouds make non-rigid motion flow estimation severely ill-posed--a challenge that existing rigid-centric methods and prior works fail to adequately address, largely because they neglect the rich Doppler velocity cues inherent in 4D radar. We propose DiFF, a generative framework that marries Doppler-informed motion priors with a Kolmogorov-Arnold Network (KAN)-based conditional flow matching model. At its core, a KAN-attention mechanism enables expressive feature extraction, while a prior-guided generative process harnesses Doppler cues to regularize the ill-posed solution space. Extensive experiments show that DiFF achieves state-of-the-art (SOTA) performance across diverse real-world datasets, reducing 3D endpoint error to the millimeter scale on the mmBody benchmark.
