Abstract
In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects. These groups follow some common physical laws that exhibit specificities that are captured through some vectorial description. We develop a model that allows us to do conditional generation from any such group given its vectorial description. Unlike previous work on learning dynamical systems that can only do trajectory completion and require a part of the trajectory dynamics to be provided as input in generation time, we do generation using only the conditioning vector with no access to generation time’s trajectories. We evaluate our model in the setting of modeling human gait and, in particular pathological human gait.
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Notes
- 1.
The first part of that trajectory will always be real data, even at test time, directly coming from the input trajectory as we will soon explain.
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Acknowledgments
This work was supported by the Swiss National Science Foundation grant number CSSII5_177179 “Modeling pathological gait resulting from motor impairment”.
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Candido Ramos, J.A., Blondé, L., Armand, S., Kalousis, A. (2021). Conditional Neural Relational Inference for Interacting Systems. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_12
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