  Motion forecasting transforms sequences of past movements and environment
context into future motion. Recent methods rely on learned representations,
resulting in hidden states that are difficult to interpret. In this work, we
use natural language to quantize motion features in a human-interpretable way,
and measure the degree to which they are embedded in hidden states. Our
experiments reveal that hidden states of motion sequences are arranged with
respect to our discrete sets of motion features. Following these insights, we
fit control vectors to motion features, which allow for controlling motion
forecasts at inference. Consequently, our method enables controlling
transformer-based motion forecasting models with textual inputs, providing a
unique interface to interact with and understand these models. Our
implementation is available at https://github.com/kit-mrt/future-motion
