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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.19170 |
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| _version_ | 1866917130442637312 |
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| author | Kraiger, Keaton Li, Jingjing Bharadwaj, Skanda Scott, Jesse Collins, Robert T. Liu, Yanxi |
| author_facet | Kraiger, Keaton Li, Jingjing Bharadwaj, Skanda Scott, Jesse Collins, Robert T. Liu, Yanxi |
| contents | 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 of those 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FootFormer: Estimating Stability from Visual Input Kraiger, Keaton Li, Jingjing Bharadwaj, Skanda Scott, Jesse Collins, Robert T. Liu, Yanxi Computer Vision and Pattern Recognition 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 of those 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. |
| title | FootFormer: Estimating Stability from Visual Input |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.19170 |