  The task of collaborative human pose forecasting stands for predicting the
future poses of multiple interacting people, given those in previous frames.
Predicting two people in interaction, instead of each separately, promises
better performance, due to their body-body motion correlations. But the task
has remained so far primarily unexplored.
  In this paper, we review the progress in human pose forecasting and provide
an in-depth assessment of the single-person practices that perform best for
2-body collaborative motion forecasting. Our study confirms the positive impact
of frequency input representations, space-time separable and fully-learnable
interaction adjacencies for the encoding GCN and FC decoding. Other
single-person practices do not transfer to 2-body, so the proposed best ones do
not include hierarchical body modeling or attention-based interaction encoding.
  We further contribute a novel initialization procedure for the 2-body spatial
interaction parameters of the encoder, which benefits performance and
stability. Altogether, our proposed 2-body pose forecasting best practices
yield a performance improvement of 21.9% over the state-of-the-art on the most
recent ExPI dataset, whereby the novel initialization accounts for 3.5%. See
our project page at https://www.pinlab.org/bestpractices2body
