MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910872606081024 |
|---|---|
| author | Romero, Diana Anwar, Fatima Elmalaki, Salma |
| author_facet | Romero, Diana Anwar, Fatima Elmalaki, Salma |
| contents | Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address this by generating synthetic yet realistic collaborative MR data. MoCoMR captures individual behavioral modalities such as speaking, gaze, and locomotion during a collaborative image-sorting task with 48 participants to identify distinct behavioral patterns. MoCoMR simulates individual actions and interactions within a virtual space, enabling researchers to investigate the impact of individual behaviors on group dynamics and task performance. This simulator facilitates the development of more effective and human-centered MR applications by providing insights into user behavior and interaction patterns. The simulator's API allows for flexible configuration and data analysis, enabling researchers to explore various scenarios and generate valuable insights for optimizing collaborative MR experiences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling Romero, Diana Anwar, Fatima Elmalaki, Salma Human-Computer Interaction Emerging Technologies Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address this by generating synthetic yet realistic collaborative MR data. MoCoMR captures individual behavioral modalities such as speaking, gaze, and locomotion during a collaborative image-sorting task with 48 participants to identify distinct behavioral patterns. MoCoMR simulates individual actions and interactions within a virtual space, enabling researchers to investigate the impact of individual behaviors on group dynamics and task performance. This simulator facilitates the development of more effective and human-centered MR applications by providing insights into user behavior and interaction patterns. The simulator's API allows for flexible configuration and data analysis, enabling researchers to explore various scenarios and generate valuable insights for optimizing collaborative MR experiences. |
| title | MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling |
| topic | Human-Computer Interaction Emerging Technologies |
| url | https://arxiv.org/abs/2503.09874 |