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CONTROLLING AND ENABLING IMPROVED CROWD SIMULATION
thesisposted on 29.04.2021, 19:48 by Caluadewa Dharshaka MathewCaluadewa Dharshaka Mathew
Authoring and Controlling virtual crowds is an important function in many domains such as Computer Graphics, Civil Engineering, and robotics. Creating a virtual crowd can be achieved by a combination of authoring tools and methods, crowd simulation algorithms, and rendering animation. However, to achieve desired virtual crowd motion and agent-based optimization the current methods and tools have limitations, that prevent low-skilled and medium-skilled users from taking advantage of them.
This dissertation formulates a novel Interactive Crowd Content Creation (ICCC) pipeline with a set of novel components and enhancements on existing components; User Observation and Sketching, Retargeting, Crowd Simulation, Animation, and Agent-based Optimization. The Sketching component of this pipeline introduces a novel sketching language validated by an extensive user study and analysis. The retargeting components allow to mix and match virtual and real trajectories, and interactively modify crowd motion spatially and temporally. An exploration-based scheme is introduced on top of the crowd simulation component to introduce the ability to model a crowd that does not have prior knowledge of their surroundings. The enhancements on the agent-based optimization allow for an accelerated scheme that allows running many simulations in parallel, model indicators, and run forward and inverse optimizations. Two applications are presented in this regard; One covering the domain of Urban Walkability and another covering the domain of Swarm Robotics. Altogether, these components and their sub-components form a comprehensive framework for authoring and controlling virtual crowds.
Our results show that this novel pipeline improves the process of crowd content creation by enabling untrained users to create complex crowd motions such as the Shibuya crossing, mix and match spatial and temporal styles. We have also improved the ability to solve for better walkable outdoor areas, and navigate swarm robots in indoor areas.