从视觉输入联合预测人体运动动态与稳定性，性能优于现有方法。
FootFormer: Estimating Stability from Visual Input
- 跨模态设计，直接从视觉数据推断运动动力学
- 在足压分布、足接触图和质心估计上均达最优或相当
- 适合动作分析、康复评估等需稳定性判断的场景
我们提出 FootFormer，一种从视觉输入中联合预测人体运动动力学的跨模态方法。在多个数据集上，FootFormer 在足压分布、足接触图及质心（CoM）估计方面，均显著优于或等同于现有仅生成其中一到两个指标的方法。此外，FootFormer 在经典生物力学指标所依赖的稳定性预测成分（如重心移动轨迹、质心、支撑基底）估计上达到当前最优性能。代码与数据见 https://github.com/keatonkraiger/Vision-to-Stability.git。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。