Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Hybrid Dynamical Models of Human Motion for the Recognition of Human Gaits
International Journal of Computer Vision
Physics-Based Person Tracking Using the Anthropomorphic Walker
International Journal of Computer Vision
Modeling human locomotion with topologically constrained latent variable models
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
A style controller for generating virtual human behaviors
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
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We propose a simple model of human motion as a switching linear dynamical system where the switches correspond to contact forces with the ground. This significantly improves the modeling performance when compared to simpler linear systems, with only marginal increase in complexity. We introduce a novel closed-form (non-iterative) algorithm to estimate the switches and learn the model parameters in between switches. We validate our model qualitatively by running simulations, and quantitatively by computing prediction errors that show significant improvements over previous approaches using linear models.