M4Human：面向人体网格重建的大规模多模态毫米波雷达基准
M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction
为视觉研究者提供了大规模毫米波雷达与RGB-D对齐的人体网格重建基准，可用于探索隐私保护、抗遮挡的人体感知，以及多模态融合（雷达点云+RGB-D）的HMR方法。
原文依据 we introduce M4Human, the largest-scale multimodal benchmark to date for radar-based HMR, featuring 661K frames
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人体网格重建（HMR）能够直接洞察人体与环境的交互，赋能多种沉浸式应用。然而，现有的大规模HMR基准主要依赖视距内的RGB传感，导致HMR系统继承了基于视觉的系统的局限性，包括对遮挡、光照变化和隐私问题的敏感性。这些局限性促使人们日益关注射频（RF）毫米波雷达作为一种隐私保护且鲁棒的人体感知模态。尽管前景广阔，当前的雷达数据集仍受限于稀疏的骨架标注、规模小以及简单的原地动作。为填补这一空白，我们推出了M4Human，这是迄今为止规模最大的基于雷达的HMR多模态基准，包含66.1万帧——比之前最大的数据集大9倍——并配有高分辨率毫米波雷达、RGB和深度数据。M4Human同时提供原始雷达张量（RT）和处理后的雷达点云（RPC），支持跨不同射频信号粒度的研究。它还包含高质量的运动捕捉（MoCap）标注，带有3D网格和全局轨迹，覆盖20名受试者和50种多样化动作，包括原地、坐姿以及自由空间的运动或康复动作。我们在RT和RPC模态上，以及与RGB-D输入的多模态融合上建立了基准。大量结果证明了M4Human在基于雷达的人体建模方面的价值，同时揭示了在快速和无约束运动下持续存在的挑战。我们的基准已公开可用，网址为 https://fanjunqiao.github.io/M4Human-site/ 。
英文原文摘要 1658 字符
Human mesh reconstruction (HMR) provides direct insights into body-environment interaction, enabling various immersive applications. However, existing large-scale HMR benchmarks largely rely on line-of-sight RGB sensing, causing HMR systems to inherit the limitations of vision-based systems, including sensitivity to occlusion, lighting variation, and privacy concerns. These limitations have motivated growing interest in radio-frequency (RF) mmWave radar as a privacy-preserving and robust modality for human sensing. Despite this promise, current radar datasets remain limited by sparse skeleton annotations, small scale, and simple in-place actions. To address this gap, we introduce M4Human, the largest-scale multimodal benchmark to date for radar-based HMR, featuring 661K frames---9 times larger than the previous largest---with high-resolution mmWave radar, RGB, and depth data. M4Human provides both raw radar tensors (RT) and processed radar point clouds (RPC), enabling research across different levels of RF signal granularity. It also includes high-quality motion capture (MoCap) annotations with 3D meshes and global trajectories, covering 20 subjects and 50 diverse actions, including in-place, seated, and free-space sports or rehabilitation movements. We establish benchmarks on RT and RPC modalities, as well as multimodal fusion with RGB-D inputs. Extensive results demonstrate the value of M4Human for radar-based human modeling while revealing persistent challenges under fast and unconstrained motion. Our benchmark is publicly available at \href https://fanjunqiao.github.io/M4Human-site/ https://fanjunqiao.github.io/M4Human-site/ .