Didn’t see that coming: a survey on non-verbal social human behavior forecasting

German Barquero, Johnny Núñez, Sergio Escalera, Zhen Xu, Wei-Wei Tu, Isabelle Guyon, Cristina Palmero
Understanding Social Behavior in Dyadic and Small Group Interactions, PMLR 173:139-178, 2022.

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 summarised 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v173-barquero22b, title = {Didn’t see that coming: a survey on non-verbal social human behavior forecasting}, author = {Barquero, German and N{\'u}{\~n}ez, Johnny and Escalera, Sergio and Xu, Zhen and Tu, Wei-Wei and Guyon, Isabelle and Palmero, Cristina}, booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions}, pages = {139--178}, year = {2022}, editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio}, volume = {173}, series = {Proceedings of Machine Learning Research}, month = {16 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v173/barquero22b/barquero22b.pdf}, url = {https://proceedings.mlr.press/v173/barquero22b.html}, 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 summarised 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.} }
Endnote
%0 Conference Paper %T Didn’t see that coming: a survey on non-verbal social human behavior forecasting %A German Barquero %A Johnny Núñez %A Sergio Escalera %A Zhen Xu %A Wei-Wei Tu %A Isabelle Guyon %A Cristina Palmero %B Understanding Social Behavior in Dyadic and Small Group Interactions %C Proceedings of Machine Learning Research %D 2022 %E Cristina Palmero %E Julio C. S. Jacques Junior %E Albert Clapés %E Isabelle Guyon %E Wei-Wei Tu %E Thomas B. Moeslund %E Sergio Escalera %F pmlr-v173-barquero22b %I PMLR %P 139--178 %U https://proceedings.mlr.press/v173/barquero22b.html %V 173 %X 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 summarised 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.
APA
Barquero, G., Núñez, J., Escalera, S., Xu, Z., Tu, W., Guyon, I. & Palmero, C.. (2022). Didn’t see that coming: a survey on non-verbal social human behavior forecasting. Understanding Social Behavior in Dyadic and Small Group Interactions, in Proceedings of Machine Learning Research 173:139-178 Available from https://proceedings.mlr.press/v173/barquero22b.html.

Related Material