You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows

• URL: http://arxiv.org/abs/2608.09579v1
• Date: Mon, 10 Aug 2026 13:15:09 GMT
• Title: You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows
• Abstract summary: radar point clouds often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed.<n> Diffusion-based alternatives can model multi-hypothesis distributions but require costly sequential denoising for each distribution sample.<n>We propose Multi-Hypothesis Normal Flow Generator (MH-G), which models pose distributions from radar point clouds using a conditional flow backbone.
• Score: 5.241472125982733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Yet existing radar methods remain deterministic, collapsing this ambiguity into a single estimate. Diffusion-based alternatives can model multi-hypothesis distributions but require costly sequential denoising for each distribution sample and lack calibrated uncertainty. We propose Multi-Hypothesis Normalizing Flow Pose Generator (MH-NFPG), which models pose distributions from radar point clouds using a conditional normalizing flow. Specifically, we combine a spatiotemporal transformer backbone with a normalizing flow that transforms a Laplace base distribution into an expressive posterior, generated in parallel through a single forward pass. Leveraging this efficiency, we outperform diffusion-based alternatives in calibration across three radar benchmarks (MM-Fi, mmRadPose, mRI), improve pose accuracy on two, and match it on the third, while achieving over 20x faster inference for applications and reducing calibration error by up to 85%. We find that calibration degrades substantially for diffusion models, whereas our flow-based approach maintains reliable coverage, also in cross-environment settings. These results demonstrate normalizing flows as a practical alternative to diffusion models for real-time, uncertainty-aware radar pose estimation. Our code will be made publicly available.

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