Davis

Hughes, Rowan T.; Ondřej, Jan; Dingliana, John

doi:10.1145/2822013.2822030

Cited by 15 publications

(4 citation statements)

References 22 publications

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“…Taking crowd simulation as an example, we see several GAN-based solutions for modeling behavior ( Gupta et al, 2018 ; Amirian et al, 2019 ), but these models only take current agent states into account. A wide range of factors affect crowd behavior, for example, cultural factors ( Fridman et al, 2013 ), density ( Hughes et al, 2015 ), and group goals ( Bruneau et al, 2014 ). Combining a similar approach to BasketballGAN, current methods could greatly improve crowd behavior and allow exploration of semi-scripted scenarios.…”

Section: Discussionmentioning

confidence: 99%

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.

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Section: Discussionmentioning

confidence: 99%

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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“…Conversely, at the microscopic level, the focus centers on individual agent characteristics and interactions among agents. Microscopic models involve the modeling of different behaviors of agents based on attributes such as an agent's velocity [11,34], visual properties [35,36], or dynamic attributes [12,13] to establish rules for each agent, thus constructing the overall group simulation. For example, the SMF [12] interprets the motion of each agent as a result of the attraction of targets on agents, avoidance forces among agents, and repulsive interactions between agents and the environment.…”

Section: Rule-based Crowd Simulation Methodsmentioning

confidence: 99%

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

Yan,

Ding,

Huang

et al. 2024

Electronics

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The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors.

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“…Vision‐based approaches can be regarded as variants of velocity‐based methods, while they can better simulate the perception‐action of human beings. These approaches include synthetic‐vision models [OPOD10, WJDL13, HOD15], perception field based models [KSH*12], gradient‐based models [DMN*17] and optimal flow based models [LCMP19], and so on. Besides, a recent microscopic crowd simulation framework has been proposed to combine existing models by optimization [vTGL*20].…”

Section: Related Workmentioning

confidence: 99%

Model‐based Crowd Behaviours in Human‐solution Space

Wei

Wang

Zhang

et al. 2023

Computer Graphics Forum

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Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.

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Davis

Cited by 15 publications

References 22 publications

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

Model‐based Crowd Behaviours in Human‐solution Space

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