Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock \textbf{C}l\textbf{I}mbing \textbf{M}ot\textbf{I}on dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://www.lidarhumanmotion.net/cimi4d/.

翻译：动作捕捉是一个长期的研究问题。尽管该领域已研究数十年，但大部分研究聚焦于步行、坐姿、舞蹈等地面运动，而攀爬等离地动作在很大程度上被忽视。作为运动和消防领域的重要动作类型，攀爬动作因其复杂的背部姿态、精细的人-物交互以及困难的全局定位而难以捕捉。由于缺乏特定数据集，学术界对攀爬动作尚未形成深入理解。为解决这一局限，我们收集了CIMI4D——一个包含12人攀爬13种不同岩壁的大型攀爬运动数据集。该数据集包含约18万帧姿态惯性测量数据、激光雷达点云、RGB视频、高精度静态点云场景及重建的场景网格。此外，我们逐帧标注了触摸岩点，以促进对人-物交互的详细探索。该数据集的核心是一个融合优化流程，用于校正因漂移和磁场影响导致的姿态偏差。为评估CIMI4D的价值，我们完成了四项任务，包括人体姿态估计（含/不含场景约束）、姿态预测和姿态生成。实验结果表明，CIMI4D对现有方法提出了巨大挑战，并提供了广泛的研究机会。我们通过http://www.lidarhumanmotion.net/cimi4d/ 与学界共享该数据集。