  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.

Machine Learning