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Transformer Networks have established themselves as the de-factostate-of-the-art for trajectory forecasting but there is currently nosystematic study on their capability to model the motion patterns of people,without interactions with other individuals nor the social context. This paperproposes the first in-depth study of Transformer Networks (TF) andBidirectional Transformers (BERT) for the forecasting of the individual motionof people, without bells and whistles. We conduct an exhaustive evaluation ofinput/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 aretop performers in predicting individual motions, definitely overcoming RNNs andLSTMs. Furthermore, they remain within a narrow margin wrt more complextechniques, which include both social interactions and scene contexts. Sourcecode will be released for all conducted experiments.

Under the Hood of Transformer Networks for Trajectory Forecasting