Diverse Synthetic Crowds in Media, Design, and Analysis

媒体、设计和分析中的多样化综合群体

• 批准号: RGPIN-2021-03541
• 负责人: Haworth, Michael
• 金额: $ 1.75万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742608
• 关键词: Diverse; Synthetic; Crowds; Media; Design

My research program addresses the increasing need for representation, equity, and inclusion in synthetic crowds. Synthetic crowds are the practice of simulating the movement, behaviours, and interactions of many humans. The simulation of humans is extremely important to several research and applied activities. This includes everything from the large scale animation of characters for film and television to the testing and recreation of safety-critical scenarios. Past approaches for achieving this are largely focused on creating compelling and naturalistic movements of normative crowds using simplified representations of humans. These models have been applied in toolchains which do not adequately address the need for evaluation, context-aware selection of models, and the inclusion of diverse simulated humans. This means that synthetic crowds are largely not representative of individuals, and if they are not representative, then designs, media, and other applications using these synthetic crowds are not representative. In the proposed program, I focus on increasing the fidelity and scope of humans and their activities in methods for crowd animation, environment design, and both environment and policy analysis. The goal of the proposed research is to radically transform the models, toolsets, and practices in synthetic crowd research and applications. The proposed methodology is three-fold. (1) The collection of a representative amount of data from people with diverse mobilities and mobility assistive devices, with rich meta-level information purpose-built for public access. This includes developing motion capture methods and rich individualistic data collection protocols that include a variety of locomotion actions, mobilities, and mobility devices. (2) A broader more diverse consideration of human mobility in simulation. This includes adopting and developing new methods in machine learning that are both data- and simulation-driven to learn diverse representations of individuals, gaits, and assistive devices for high-fidelity physically simulated humans, while simultaneously learning crowd interactions not captured by the data. (3) The augmentation and redesign of industry tool chains. This includes developing new methods in design and analytics which enable practitioners and policymakers to explore the accessibility and equity of their designs. Central to this effort is the development of tools for both industry and communities to investigate and design better environments through inclusive practices, such as participatory and/or community-driven design and analysis. This also includes the development of games and augmented approaches to design and analysis. These approaches promise to rapidly expand engineering capabilities while increasing inclusivity in a historically non-inclusive design pipelines.

我的研究计划解决了对代表性、公平性和融入合成人群的日益增长的需求。合成人群是模拟许多人的运动、行为和互动的实践。对人体的模拟对于一些研究和应用活动是极其重要的。这包括从电影和电视角色的大规模动画到安全关键场景的测试和重现。过去实现这一目标的方法主要集中在使用简化的人类表现来创造令人信服的、自然主义的规范人群运动。这些模型被应用在工具链中，这些工具链不能充分解决评估、模型的上下文感知选择以及包含不同的模拟人的需求。这意味着合成群体在很大程度上不能代表个人，如果他们不具有代表性，那么使用这些合成群体的设计、媒体和其他应用程序就不具有代表性。在拟议的项目中，我专注于在人群动画、环境设计以及环境和政策分析的方法上增加人类及其活动的保真度和范围。拟议研究的目标是从根本上改变合成人群研究和应用中的模型、工具集和实践。拟议的方法有三个方面。(1)从具有不同移动性和移动辅助设备的人那里收集具有代表性的数据量，并具有专门为公众访问而构建的丰富的元级信息。这包括开发运动捕获方法和丰富的个性化数据收集协议，其中包括各种移动动作、移动性和移动设备。(2)在仿真中更广泛、更多样化地考虑人的流动性。这包括采用和开发机器学习中的新方法，这些方法既是数据驱动的，也是模拟驱动的，以学习高保真物理模拟人类的个人、步态和辅助设备的不同表示，同时学习数据未捕获的人群互动。(3)行业工具链的扩充和重新设计。这包括开发设计和分析的新方法，使从业者和政策制定者能够探索其设计的可及性和公平性。这一努力的核心是为行业和社区开发工具，通过参与性和/或社区主导的设计和分析等包容性做法，调查和设计更好的环境。这还包括开发游戏以及增强设计和分析方法。这些方法承诺迅速扩展工程能力，同时增加历史上非包容性设计管道的包容性。