  We present AROS, a one-shot learning approach that uses an explicit
representation of interactions between highly-articulated human poses and 3D
scenes. The approach is one-shot as the method does not require re-training to
add new affordance instances. Furthermore, only one or a small handful of
examples of the target pose are needed to describe the interaction. Given a 3D
mesh of a previously unseen scene, we can predict affordance locations that
support the interactions and generate corresponding articulated 3D human bodies
around them. We evaluate on three public datasets of scans of real environments
with varied degrees of noise. Via rigorous statistical analysis of crowdsourced
evaluations, results show that our one-shot approach outperforms data-intensive
baselines by up to 80\%.
