This paper introduces ELMO, a real-time upsampling motion capture framework designed for a single LiDAR sensor. Modeled as a conditional autoregressive transformer-based upsampling motion generator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point cloud sequence. The key feature of ELMO is the coupling of the self-attention mechanism with thoughtfully designed embedding modules for motion and point clouds, significantly elevating the motion quality. To facilitate accurate motion capture, we develop a one-time skeleton calibration model capable of predicting user skeleton offsets from a single-frame point cloud. Additionally, we introduce a novel data augmentation technique utilizing a LiDAR simulator, which enhances global root tracking to improve environmental understanding. To demonstrate the effectiveness of our method, we compare ELMO with state-of-the-art methods in both image-based and point cloud-based motion capture. We further conduct an ablation study to validate our design principles. ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios. Furthermore, we contribute a high-quality LiDAR-mocap synchronized dataset comprising 20 different subjects performing a range of motions, which can serve as a valuable resource for future research. The dataset and evaluation code are available at {\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}

翻译：本文介绍ELMO，一种专为单激光雷达传感器设计的实时上采样运动捕捉框架。ELMO被建模为基于条件自回归Transformer的上采样运动生成器，能够从20 fps的激光雷达点云序列中实现60 fps的运动捕捉。其核心特点在于将自注意力机制与精心设计的运动及点云嵌入模块相结合，显著提升了运动质量。为实现精确运动捕捉，我们开发了一次性骨架校准模型，能够从单帧点云预测用户骨架偏移量。此外，我们引入了一种利用激光雷达模拟器的新型数据增强技术，通过增强全局根节点追踪来提升环境理解能力。为验证方法的有效性，我们将ELMO与基于图像和点云的最先进运动捕捉方法进行对比，并通过消融实验验证设计原则。ELMO的快速推理时间使其非常适合实时应用，演示视频中展示了其在直播和交互游戏场景中的表现。我们同时贡献了一个包含20名受试者执行多种动作的高质量激光雷达-动作捕捉同步数据集，可为未来研究提供宝贵资源。数据集与评估代码已公开于{\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}。