  Current datasets to train social behaviors are usually borrowed from
surveillance applications that capture visual data from a bird's-eye
perspective. This leaves aside precious relationships and visual cues that
could be captured through a first-person view of a scene. In this work, we
propose a strategy to exploit the power of current game engines, such as Unity,
to transform pre-existing bird's-eye view datasets into a first-person view, in
particular, a depth view. Using this strategy, we are able to generate large
volumes of synthetic data that can be used to pre-train a social navigation
model. To test our ideas, we present DeepSocNav, a deep learning based model
that takes advantage of the proposed approach to generate synthetic data.
Furthermore, DeepSocNav includes a self-supervised strategy that is included as
an auxiliary task. This consists of predicting the next depth frame that the
agent will face. Our experiments show the benefits of the proposed model that
is able to outperform relevant baselines in terms of social navigation scores.
