2023
DOI: 10.3389/frobt.2023.1076780
|View full text |Cite
|
Sign up to set email alerts
|

AROS: Affordance Recognition with One-Shot Human Stances

Abstract: We present Affordance Recognition with One-Shot Human Stances (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 since it does not require iterative training or retraining 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 locatio… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 44 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?