用分阶段方法提升毫米波雷达人体三维建模精度
A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery
- 先提取人体反射信号，再融合动态信息重建网格
- 在多个数据集上准确率优于现有方法，计算高效
- 适合做雷达感知与动作识别的科研人员参考
毫米波雷达因其在恶劣环境下的鲁棒性和良好的隐私保护特性，成为人体感知的有前景技术。然而，由于信号杂波严重且雷达测量具有本质上的不完整性，从雷达观测中恢复精确的3D人体网格仍具挑战。以往方法通常采用端到端框架，直接从原始雷达数据回归人体参数，未解耦信号解读与几何推理，也未利用时间运动线索，限制了性能。为此，我们提出一种两阶段雷达人体重建框架：首先，设计人体反射提取模块，通过粗到精定位与体素级分割，生成带置信度的雷达体数据，编码体素级人体概率；其次，构建运动感知网格恢复网络，通过双分支结构联合建模每帧几何与帧间动态，实现人体重建。大量实验表明，该方法在多个基准上优于现有方法，同时保持计算效率。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental conditions and strong privacy-preserving properties. However, recovering accurate 3D human body meshes from radar observations remains difficult due to severe signal clutter and the inherently partial nature of radar measurements. Previous works typically adopt end-to-end frameworks that directly regress human body parameters from raw radar data, without decoupling signal interpretation from geometric reasoning or exploiting temporal motion cues, limiting learning performance. To address this, we propose a two-stage framework for radar-based human body reconstruction. First, we introduce a human reflection extraction module that performs coarse-to-fine localization and voxel-wise segmentation to produce a confidence-weighted radar volume encoding voxel-level human likelihood. Second, we design a motion-aware mesh recovery network that reconstructs the human body by jointly modeling per-frame geometry and inter-frame dynamics using a dual-branch architecture. Extensive experiments demonstrate that the proposed method outperforms existing approaches while maintaining computational efficiency.
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