# UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup

arXiv: https://arxiv.org/abs/2208.04598 · HTML: https://mlsanity.com/paper/2208.04598/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 16 of 16)

- [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)
- [Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis](https://mlsanity.com/paper/2502.19056/) (2025)
- [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)
- [Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation](https://mlsanity.com/paper/2511.22184/) (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)
- [A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors](https://mlsanity.com/paper/2603.28784/) (2026)
- [SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation](https://mlsanity.com/paper/2609.08108/) (2026)
- [CleanMDM: Clean Motion Diffusion Model for Multimodal Motion Cleanup](https://mlsanity.com/paper/2610.05411/) (2026)

## Abstract (arXiv)

Human motion synthesis and editing are essential to many applications like film post-production. However, they often introduce artefacts in motions, which can be detrimental to the perceived realism. In particular, footskating is a frequent and disturbing artefact requiring foot contacts knowledge to be cleaned up. Current approaches to obtain foot contact labels rely either on unreliable threshold-based heuristics or on tedious manual annotation. In this article, we address foot contact label detection from motion with a deep learning. To this end, we first publicly release UnderPressure, a novel motion capture database labelled with pressure insoles data serving as reliable knowledge of foot contact with the ground. Then, we design and train a deep neural network to estimate ground reaction forces exerted on the feet from motion data and then derive accurate foot contact labels. The evaluation of our model shows that we significantly outperform heuristic approaches based on height and velocity thresholds and that our approach is much more robust on motion sequences suffering from perturbations like noise or footskate. We further propose a fully automatic workflow for footskate cleanup: foot contact labels are first derived from estimated ground reaction forces. Then, footskate is removed by solving foot constraints through an optimisation-based inverse kinematics (IK) approach that ensures consistency with the estimated ground reaction forces. Beyond footskate cleanup, both the database and the method we propose could help to improve many approaches based on foot contact labels or ground reaction forces, including inverse dynamics problems like motion reconstruction and learning of deep motion models in motion synthesis or character animation. Our implementation, pre-trained model as well as links to database can be found at https://github.com/InterDigitalInc/UnderPressure.