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

• URL: http://arxiv.org/abs/2203.02480v1
• Date: Fri, 4 Mar 2022 18:25:30 GMT
• Title: Didn't see that coming: a survey on non-verbal social human behavior forecasting
• Authors: German Barquero and Johnny N\'u\~nez and Sergio Escalera and Zhen Xu and Wei-Wei Tu and Isabelle Guyon and Cristina Palmero
• Abstract summary: 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. 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.
• Score: 47.99589136455976
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Human Action Anticipation: A Survey [86.415721659234]
  The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation.
  arXiv  Detail & Related papers  (2024-10-17T21:37:40Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• Human Motion Generation: A Survey [67.38982546213371]
  Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications. Most research within this field focuses on generating human motions based on conditional signals, such as text, audio, and scene contexts. We present a comprehensive literature review of human motion generation, which is the first of its kind in this field.
  arXiv  Detail & Related papers  (2023-07-20T14:15:20Z)
• Gaze-based intention estimation: principles, methodologies, and applications in HRI [0.0]
  This review aims to draw a line between insights in the psychological literature on visuomotor control and relevant applications of gaze-based intention recognition. The use of eye tracking and gaze-based models for intent recognition in Human-Robot Interaction is considered.
  arXiv  Detail & Related papers  (2023-02-09T09:44:13Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions [6.531659195805749]
  Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts. We investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media.
  arXiv  Detail & Related papers  (2021-09-01T16:28:24Z)
• Prevention and Resolution of Conflicts in Social Navigation -- a Survey [39.89946101238849]
  Recent developments in robotics have encountered and tackled some of the challenges of navigating in mixed human-robot environments. Many of the relevant papers are not comparable and there is no standard vocabulary between the researchers. This paper propose some future directions and problems that are currently in the frontier of social navigation to help focus research efforts.
  arXiv  Detail & Related papers  (2021-06-23T01:10:22Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.