Animating agents based on radial view in crowd simulation

Li, Fu-Shun; Wong, Sai‐Keung

doi:10.1145/2993369.2993373

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…Pursuits is particularly relevant to VR; the dynamic nature of VR applications often requires selecting moving targets. For example, when communicating with avatars of virtual human agents in VR, a common approach is to point at them while they move [19,22,36]. Selection of moving targets is common in VR games, and scientific simulations where users can, for example, select an object to track its development [10,11,34].…”

Section: Introductionmentioning

confidence: 99%

VRpursuits

Khamis

Oechsner

Alt

et al. 2018

Proceedings of the 2018 International Conference on Advanced Visual Interfaces

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Figure 1: We investigate the selection of moving 3D targets in virtual environments (A) using smooth pursuit eye movements (arrows are for illustration only and were not shown to users). We study how parameters specific to VR settings influence the performance. We then develop and evaluate two sample applications: (B) a virtual ATM where users authenticate by following the digits with their eyes, and (C) a space shooting game where users blast asteroids by following them.

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Section: Introductionmentioning

confidence: 99%

VRpursuits

Khamis

Oechsner

Alt

et al. 2018

Proceedings of the 2018 International Conference on Advanced Visual Interfaces

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“…A semantic virtual environment can support heterogeneous behaviors of crowds 19 . There are rich crowd simulation techniques 20‐23 that can be adopted in our system.…”

Section: Related Workmentioning

confidence: 99%

Generation of small groups with rich behaviors from natural language interface

Chen

Wong

Liu

2020

Computer Animation & Virtual

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In this article, we develop a system with a natural language interface to generate animation of small groups in environments with ambient crowds. Our system takes simple sentences of English as input and these sentences are parsed to obtain information about character attributes, behaviors, and locations for constructing situation nodes. These situation nodes form an animation graph for producing an animation of small groups. The interface assists in interactively managing and editing the animation graph. We demonstrate the effectiveness of the system in some examples. The user study results indicate that the proposed system is user-friendly and flexible in producing animation of small groups in various scenes. K E Y W O R D S crowd animation, interactive interface, natural language processing, small groups 1 INTRODUCTION Crowds are composed of individual characters and small groups (e.g., families, friends) which exhibit rich behaviors, e.g., watching a performance, wandering, and dancing, that are common in various situations, e.g., parks, subway stations, streets, markets, and exhibition halls. Graphical authoring tools and behavior trees have been adopted in producing animations with character models. 1,2 Natural language can be treated as a kind of input medium for constructing virtual objects and animations. 3-5 A story maker tool incorporates natural language processing (NLP) and graphics to generate 2D animated scenes. 3 The components of a scene, including actors, actions, objects, and locations, are determined based on input sentences. A story telling system 5 adopts a module, called subject-predicate-object (SPO), 6 to extract the subject, predicate and object in an input sentence. The aforementioned systems mainly focus on one or a few characters, which do not handle groups, crowd behaviors, and groups of ambient crowds. In this article, we implement a system with a natural language interface and crowd simulation techniques to animate groups (Figure 1). We extract group-related information, such as characters, group behaviors, group targets, and locations from the input sentences. Situation nodes are constructed to form an entire animation. In this study, we take input as simple sentences of English. A user study was conducted to evaluate the performance and utility of our system.

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“…Furthermore, the agent should consider the appropriate global paths for moving to a target while collision avoidance is achieved. To locally determine the final movement direction of agents, we can adopt the navigation algorithms, such as reciprocal velocity obstacle (RVO) (Van den Berg et al 2008), radial view based method (Wong 2016), vision-based method (Dutra et al 2017), and predefined evacuation routes (Wong et al 2017).…”

Section: Durations: How Long Does a Hazard Last?mentioning

confidence: 99%