DiFF is a generative framework that uses Doppler velocity cues from 4D millimeter-wave radar to improve human motion flow estimation. It combines Doppler-informed motion priors with a Kolmogorov‑Arnold Network (KAN) based conditional flow matching model, featuring a KAN‑attention mechanism for expressive feature extraction. Experiments demonstrate that DiFF achieves state‑of‑the‑art performance, reducing 3D endpoint error to the millimeter scale on the mmBody benchmark.
By Kai Wang, Mingle Zhao
arXiv:2512. 17897v2 Announce Type: replace-cross Abstract: We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery.
By Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany
arXiv:2605. 00242v2 Announce Type: replace-cross Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation.
By Xijia Wei, Yuan Fang, Kevin Chetty, Youngjun Cho, Nadia Bianchi-Berthouze
arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
By Runze Gan, Qing Li, Simon J. Godsill, Mike E. Davies, James R. Hopgood
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds.
DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.
By Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany