# GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics

arXiv: https://arxiv.org/abs/2310.03930 · HTML: https://mlsanity.com/paper/2310.03930/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2023.

## Built on by (most cited first, 13 of 13)

- [StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework](https://mlsanity.com/paper/2405.05691/) (2024)
- [AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale](https://mlsanity.com/paper/2406.18537/) (2024)
- [ImDy: Human Inverse Dynamics from Imitated Observations](https://mlsanity.com/paper/2410.17610/) (2024)
- [Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors](https://mlsanity.com/paper/2603.16233/) (2026)
- [Homogeneous Dynamics Space for Heterogeneous Humans](https://mlsanity.com/paper/2412.06146/) (2024)
- [Physics-informed Ground Reaction Dynamics from Human Motion Capture](https://mlsanity.com/paper/2507.01340/) (2025)
- [FootFormer: Estimating Stability from Visual Input](https://mlsanity.com/paper/2510.19170/) (2025)
- [Gravity-guided Contact Dynamics Estimation from 3D Human Motions](https://mlsanity.com/paper/2606.08133/) (2026)
- [PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation](https://mlsanity.com/paper/2606.26741/) (2026)
- [Towards Real-World Wearable Motion Reconstruction](https://mlsanity.com/paper/2607.09780/) (2026)
- [Step2Motion: Locomotion Reconstruction from Pressure Sensing Insoles](https://mlsanity.com/paper/2510.22712/) (2025)
- [Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets](https://mlsanity.com/paper/2511.11713/) (2025)
- [Shared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals](https://mlsanity.com/paper/2603.25906/) (2026)

## Abstract (arXiv)

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/.