  Transformer Networks have established themselves as the de-facto
state-of-the-art for trajectory forecasting but there is currently no
systematic study on their capability to model the motion patterns of people,
without interactions with other individuals nor the social context. This paper
proposes the first in-depth study of Transformer Networks (TF) and
Bidirectional Transformers (BERT) for the forecasting of the individual motion
of people, without bells and whistles. We conduct an exhaustive evaluation of
input/output representations, problem formulations and sequence modeling,
including a novel analysis of their capability to predict multi-modal futures.
Out of comparative evaluation on the ETH+UCY benchmark, both TF and BERT are
top performers in predicting individual motions, definitely overcoming RNNs and
LSTMs. Furthermore, they remain within a narrow margin wrt more complex
techniques, which include both social interactions and scene contexts. Source
code will be released for all conducted experiments.
