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We present AROS, a one-shot learning approach that uses an explicitrepresentation of interactions between highly-articulated human poses and 3Dscenes. The approach is one-shot as the method does not require re-training toadd new affordance instances. Furthermore, only one or a small handful ofexamples of the target pose are needed to describe the interaction. Given a 3Dmesh of a previously unseen scene, we can predict affordance locations thatsupport the interactions and generate corresponding articulated 3D human bodiesaround them. We evaluate on three public datasets of scans of real environmentswith varied degrees of noise. Via rigorous statistical analysis of crowdsourcedevaluations, results show that our one-shot approach outperforms data-intensivebaselines by up to 80\%.

AROS: Affordance Recognition with One-Shot Human Stances