WiCompass: Oracle-driven Data Scaling for mmWave Human Pose Estimation

TL;DR

WiCompass introduces a coverage-aware data collection framework that leverages an oracle to improve out-of-distribution robustness in mmWave human pose estimation, outperforming traditional data scaling methods.

Contribution

The paper presents WiCompass, a novel oracle-driven framework for targeted data collection that enhances OOD robustness in mmWave HPE by focusing on dataset coverage.

Findings

WiCompass improves OOD accuracy at fixed data budgets.

WiCompass exhibits better scaling behavior than conventional methods.

Coverage-aware data collection outperforms brute-force scaling.

Abstract

Millimeter-wave Human Pose Estimation (mmWave HPE) promises privacy but suffers from poor generalization under distribution shifts. We demonstrate that brute-force data scaling is ineffective for out-of-distribution (OOD) robustness; efficiency and coverage are the true bottlenecks. To address this, we introduce WiCompass, a coverage-aware data-collection framework. WiCompass leverages large-scale motion-capture corpora to build a universal pose space ``oracle'' that quantifies dataset redundancy and identifies underrepresented motions. Guided by this oracle, WiCompass employs a closed-loop policy to prioritize collecting informative missing samples. Experiments show that WiCompass consistently improves OOD accuracy at matched budgets and exhibits superior scaling behavior compared to conventional collection strategies. By shifting focus from brute-force scaling to coverage-aware data acquisition, this work offers a practical path toward robust mmWave sensing.