Abstract
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues.
Original language | English |
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Book series | Proceedings of Machine Learning Research |
Volume | 173 |
Pages (from-to) | 139-178 |
Number of pages | 40 |
ISSN | 2640-3498 |
Publication status | Published - 2021 |
Event | ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021 - Virtual, Online Duration: 16 Oct 2021 → … |
Conference
Conference | ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021 |
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City | Virtual, Online |
Period | 16/10/2021 → … |
Bibliographical note
Funding Information:Isabelle Guyon was supported by ANR Chair of Artificial Intelligence HUMANIA ANR-19-CHIA-0022. This work has been partially supported by the Spanish project PID2019-105093GB-I00 and by ICREA under the ICREA Academia programme.
Publisher Copyright:
© 2022 G. Barquero, J. Núñez, S. Escalera, Z. Xu, W.-W. Tu, I. Guyon & C. Palmero.
Keywords
- Backchanneling
- Behavior forecasting
- Dyadic interactions
- Engagement
- Human motion prediction
- Multiparty interactions
- Social robots
- Social signal prediction
- Socially interactive agents
- Triadic interactions