Diverse Synthetic Crowds in Media, Design, and Analysis

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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.