  Anticipating human motion depends on two factors: the past motion and the
person's intention. While the first factor has been extensively utilized to
forecast short sequences of human motion, the second one remains elusive. In
this work we approximate a person's intention via a symbolic representation,
for example fine-grained action labels such as walking or sitting down.
Forecasting a symbolic representation is much easier than forecasting the full
body pose with its complex inter-dependencies. However, knowing the future
actions makes forecasting human motion easier. We exploit this connection by
first anticipating symbolic labels and then generate human motion, conditioned
on the human motion input sequence as well as on the forecast labels. This
allows the model to anticipate motion changes many steps ahead and adapt the
poses accordingly. We achieve state-of-the-art results on short-term as well as
on long-term human motion forecasting.
