AUTHOR=Pacheco-Ortega Abel , Mayol-Cuevas Walterio 

TITLE=AROS: Affordance Recognition with One-Shot Human Stances

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2023.1076780

DOI=10.3389/frobt.2023.1076780

ISSN=2296-9144

ABSTRACT=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 iterative training or 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 interactions. 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, our results show that our one-shot approach is preferred up to 80{\%} of the time over data-intensive baselines.