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

2026-08-10

AI Summary

Millimeter-wave radar point clouds are inherently sparse and noisy, leading to ambiguities in human pose estimation that deterministic methods struggle to capture. To address this challenge, this work proposes the Multi-Hypothesis Normalizing Flow Pose Generator (MH-NFPG), which introduces conditional normalizing flows into radar-based pose estimation for the first time. By integrating a spatio-temporal Transformer backbone with a Laplace base distribution, MH-NFPG generates diverse, multimodal pose hypotheses in parallel through a single forward pass. Evaluated on three benchmarks—MM-Fi, mmRadPose, and mRI—the method reduces calibration error by up to 85%, achieves over 20× faster inference, and demonstrates significantly improved pose accuracy on two datasets while matching state-of-the-art performance on the third. Moreover, it maintains reliable coverage under cross-environment settings.

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.

Problem

• radar pose estimation
• multi-hypothesis
• uncertainty calibration
• normalizing flows
• ill-posed problem

Innovation

• Normalizing Flows
• Radar Pose Estimation
• Multi-Hypothesis Modeling
• Uncertainty Calibration
• Real-Time Inference