The physical plausibility of human motions is vital to various applications in fields including but not limited to graphics, animation, robotics, vision, biomechanics, and sports science. While fully simulating human motions with physics is an extreme challenge, we hypothesize that we can treat this complexity as a black box in a data-driven manner if we focus on the ground contact, and have sufficient observations of physics and human activities in the real world. To prove our hypothesis, we present GroundLink, a unified dataset comprised of captured ground reaction force (GRF) and center of pressure (CoP) synchronized to standard kinematic motion captures. GRF and CoP of GroundLink are not simulated but captured at high temporal resolution using force platforms embedded in the ground for uncompromising measurement accuracy. This dataset contains 368 processed motion trials (~1.59M recorded frames) with 19 different movements including locomotion and weight-shifting actions such as tennis swings to signify the importance of capturing physics paired with kinematics. GroundLinkNet, our benchmark neural network model trained with GroundLink, supports our hypothesis by predicting GRFs and CoPs accurately and plausibly on unseen motions from various sources. The dataset, code, and benchmark models are made public for further research on various downstream tasks leveraging the rich physics information at https://csr.bu.edu/groundlink/.

翻译：人体运动的物理合理性对于图形学、动画、机器人学、视觉、生物力学及运动科学等领域中的各类应用至关重要。尽管完全基于物理模拟人体运动是一项极大挑战，但我们假设，若能聚焦于地面接触并拥有真实世界物理与人类活动的充足观测数据，便可通过数据驱动方式将该复杂性视为黑箱处理。为验证这一假设，我们提出了GroundLink——一个统一的数据集，包含与标准运动捕捉同步采集的地面反作用力（GRF）和压力中心（CoP）数据。GroundLink中的GRF和CoP并非模拟生成，而是通过嵌入地面的力台以高时间分辨率捕捉，确保测量精度不受影响。该数据集包含368个经过处理的运动序列（约159万帧记录），涵盖19种不同动作，包括行走、重心转移及如网球挥拍等动作，以突显同步捕捉物理量与运动学数据的重要性。基于GroundLink训练的基准神经网络模型GroundLinkNet，能够准确且合理地预测来自多种来源的未知动作的GRF和CoP，从而支持了我们的假设。数据集、代码及基准模型已在https://csr.bu.edu/groundlink/ 公开，以供利用该丰富物理信息开展各类下游任务研究。