mmWaveFlow: Unified Enhancement and Generation of mmWave Human Point Clouds

Millimeter-wave (mmWave) point clouds have attracted growing interest in human sensing due to their robustness, privacy preservation, and low cost. However, their practical use is hindered by the inherent sparsity of data and the lack of large-scale data. We revisit generative modeling for mmWave point clouds and propose a unified flow-matching framework mmWaveFlow that unifies enhancement and generation by learning an invertible transport between dense and sparse point clouds. We leverage paired data and a latent-alignment module to enforce semantic alignment and bridge the modality gap. We find that condition-free flow matching is more vulnerable to latent path crossings, which impair bidirectional transport. Therefore, we propose Origin-Aware Flow Matching (OA-Flow), which conditioning transport on the origin of the path mitigates ambiguity in bidirectional transport. Results of experiments across multiple datasets demonstrate the effectiveness of mmWaveFlow for mmWave human point clouds generation and enhancement. We also observe consistent gains in downstream tasks, highlighting the promise of our framework for human sensing. We will release the code.