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Under the Hood of Transformer Networks for Trajectory Forecasting 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