Didn't see that coming: a survey on non-verbal social human behavior forecasting
This work addresses the need for a cohesive approach to behavior forecasting in human-robot interaction and socially-aware motion generation, though it is incremental as it synthesizes existing research rather than introducing new methods.
This survey paper tackles the problem of non-verbal social human behavior forecasting by proposing a unified framework that connects social signals prediction and human motion forecasting, identifying shared challenges like future stochasticity and context awareness, and providing a taxonomy of methods from the last 5 years along with dataset and metric overviews.
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.