AROS: Affordance Recognition with One-Shot Human Stances

• URL: http://arxiv.org/abs/2210.11725v1
• Date: Fri, 21 Oct 2022 04:29:21 GMT
• Title: AROS: Affordance Recognition with One-Shot Human Stances
• Authors: Abel Pacheco-Ortega and Walterio Mayol-Cuevas
• Abstract summary: We present AROS, a one-shot learning approach that uses an explicit representation of interactions between human poses and 3D scenes. 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. Results show that our one-shot approach outperforms data-intensive baselines by up to 80%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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\%.

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