FootFormer: Estimating Stability from Visual Input


Keaton Yukio Kraiger (Pennsylvania State University), Jingjing Li (Pennsylvania State University), Skanda Bharadwaj (Pennsylvania State University), Jesse Scott (Scientific Applications & Research Associates (SARA), Inc.), Robert T. Collins (Pennsylvania State University), Yanxi Liu (Pennsylvania State University)
The 35th British Machine Vision Conference
PDFPosterSupplementary

Abstract

We propose FootFormer, a cross-modality approach for jointly predicting human motion dynamics directly from visual input. On multiple datasets, FootFormer achieves statistically significantly better or equivalent estimates of foot pressure distributions, foot contact maps, and center of mass (CoM), as compared with existing methods that generate one or two measures. Furthermore, FootFormer achieves SOTA performance in estimating stability-predictive components (CoP, CoM, BoS) used in classic kinesiology metrics. Code and data are available at https://github.com/keatonkraiger/Vision-to-Stability.git.

Citation

@inproceedings{Kraiger_2025_BMVC,
author    = {Keaton Yukio Kraiger and Jingjing Li and Skanda Bharadwaj and Jesse Scott and Robert T. Collins and Yanxi Liu},
title     = {FootFormer: Estimating Stability from Visual Input},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_865/paper.pdf}
}


Copyright © 2025 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection