A Crowd Modeling Framework for Socially Plausible Animation Behaviors

Park, Seung In; Peng, Chao; Quek, Francis; Cao, Yong

doi:10.1007/978-3-642-34710-8_14

Cited by 2 publications

(2 citation statements)

References 30 publications

(34 reference statements)

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“…Due to the relevant role played by emergency simulations in the development of the field, variables such as 'level of panic', for instance, have been critical, strongly impacting the behavior of the virtual characters (Braun et al [1], Tsai et al [23]). The complexity of psychological traits varies from these simple 'stress' emotional states (Park et al [15]) to models where individuals differ from each other in their personality (Li et al. [12]).…”

Section: State Of the Artmentioning

confidence: 99%

Bio-Inspired Virtual Populations

Antunes

Thalmann

2016

Proceedings of the 29th International Conference on Computer Animation and Social Agents

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In this paper, we Secdescribe an agency model for generative populations of humanoid characters, based upon temporal variation of affective states. We have built on an existing agent framework from Sequeira et al. [18], and adapted it to be susceptible to temperamental and emotive states in the context of cooperative and non-cooperative interactions based on trading activity. More specifically, this model operates within two existing frameworks: a) intrinsically motivated reinforcement learning, structured upon affective appraisals in the relationship of the agents with their environment [20,18]; b) a multi-temporal representation of individual psychology, common in the field of affective computing, structuring individual psychology as a tripartite relationship: emotions-moods-personality [8,16]. Results show a populations of agents that express their individuality and autonomy with a high level of heterogeneous and spontaneous behaviors, while simultaneously adapting and overcoming their perceptual limitations.

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Section: State Of the Artmentioning

confidence: 99%

Bio-Inspired Virtual Populations

Antunes

Thalmann

2016

Proceedings of the 29th International Conference on Computer Animation and Social Agents

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“…A crowd simulator uses macroscopic algorithms (e.g., continuum crowds [22], aggregate dynamics [23], vector fields [24], and navigation fields [25]) or microscopic algorithms (e.g., morphable crowds [26] and socially plausible behaviors [27]) to create crowd motions and interactions. Outcomes of the simulator are usually a successive sequence of time frames, and each frame contains arrays of positions and orientations in the 3D virtual environment.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Large Crowd Rendering with Efficient Character and Instance Management on GPU

Dong

Peng

2019

International Journal of Computer Games Technology

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Achieving the efficient rendering of a large animated crowd with realistic visual appearance is a challenging task when players interact with a complex game scene. We present a real-time crowd rendering system that efficiently manages multiple types of character data on the GPU and integrates seamlessly with level-of-detail and visibility culling techniques. The character data, including vertices, triangles, vertex normals, texture coordinates, skeletons, and skinning weights, are stored as either buffer objects or textures in accordance with their access requirements at the rendering stage. Our system preserves the view-dependent visual appearance of individual character instances in the crowd and is executed with a fine-grained parallelization scheme. We compare our approach with the existing crowd rendering techniques. The experimental results show that our approach achieves better rendering performance and visual quality. Our approach is able to render a large crowd composed of tens of thousands of animated instances in real time by managing each type of character data in a single buffer object.

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A Crowd Modeling Framework for Socially Plausible Animation Behaviors

Cited by 2 publications

References 30 publications

Bio-Inspired Virtual Populations

Bio-Inspired Virtual Populations

Real-Time Large Crowd Rendering with Efficient Character and Instance Management on GPU

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