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SAUCE: Asset Libraries of the Future

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... Moreover, few tools currently exist for rapidly prototyping crowd simulations, which can often be a crucial task in a dynamic production environment. Recent developments have explored the use of smart assets [26], based on the use of machine learning to automatically generate semantic information. We predict that future crowd simulation systems will take advantage of semantic information to move towards individually autonomous agents, leading to more convincing individual and emergent behavior and facilitate the re-use of existing crowds as a valuable addition to crowd simulation software. ...
... • The storage format should support rich data structures with contextual data, allowing for different interpretations • The storage format should be language independent. The presented system uses the JSON file format, with JSON schemas providing data validation, as this approach has been shown to work well for classifying large asset stores [26]. Therefore, the toolset consists of an API with a corresponding Unity interface which allows the user to create a JSON schema, create JSON files with a structure given by a schema, validate the JSON files against the schema, and finally parse the JSON files into a C# object. ...
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Crowd simulation is the act of simulating and controlling the dynamic movement of large groups of virtual characters. Crowd simulation is traditionally a complex and time-consuming process, requiring extensive manual effort to achieve. On the one hand, commercial and liberally licensed tools tend to have many aspects of simulation tightly integrated which can be prohibitively difficult to re-configure, on the other, paying extras can be far more costly. In this context, the re-use of existing simulated crowds has been identified as a valuable cost-saving approach to crowd simulations. Previous approaches have investigated the use of environment semantics , but they have not been integrated with a commonly used simulation platform, rendering their usefulness limited. We present a novel approach to crowd simulation using an emergent system for re-targeting autonomous crowds and report on the findings of a problem discovery study, analyzing and establishing key aspects of functionality, usability, and user experience. Our results provide a breakdown of the crowd simulation process with corresponding time-on-task metrics to provide a reference point for future scientific research into crowd simulation systems. Furthermore, we report on how users react to a system that involves the use of semantic data to facilitate the re-use of existing crowd simulations. We anticipate that other researchers will follow suit, to develop tools that are both innovative and usable in crowd simulation practices.
... Moreover, few tools currently exist for rapidly prototyping crowd simulations, which can often be a crucial task in a dynamic production environment. Recent developments have explored the use of smart assets [26], based on the use of machine learning to automatically generate semantic information. We predict that future crowd simulation systems will take advantage of semantic information to move towards individually autonomous agents, leading to more convincing individual and emergent behavior and facilitate the re-use of existing crowds as a valuable addition to crowd simulation software. ...
... • The storage format should support rich data structures with contextual data, allowing for different interpretations • The storage format should be language independent. The presented system uses the JSON file format, with JSON schemas providing data validation, as this approach has been shown to work well for classifying large asset stores [26]. Therefore, the toolset consists of an API with a corresponding Unity interface which allows the user to create a JSON schema, create JSON files with a structure given by a schema, validate the JSON files against the schema, and finally parse the JSON files into a C# object. ...
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Crowd simulation is the act of simulating and controlling the dynamic movement of large groups of virtual characters. Crowd simulation is traditionally a complex and time-consuming process, requiring extensive manual effort to achieve. On the one hand, commercial and liberally licensed tools tend to have many aspects of simulation tightly integrated which can be prohibitively difficult to re-configure, on the other, paying extras can be far more costly. In this context, the re-use of existing simulated crowds has been identified as a valuable cost-saving approach to crowd simulations. Previous approaches have investigated the use of environment semantics, but they have not been integrated with a commonly used simulation platform, rendering their usefulness limited. We present a novel approach to crowd simulation using an emergent system for re-targeting autonomous crowds and report on the findings of a problem discovery study, analyzing and establishing key aspects of functionality, usability, and user experience. Our results provide a breakdown of the crowd simulation process with corresponding time-on-task metrics to provide a reference point for future scientific research into crowd simulation systems. Furthermore, we report on how users react to a system that involves the use of semantic data to facilitate the re-use of existing crowd simulations. We anticipate that other researchers will follow suit, to develop tools that are both innovative and usable in crowd simulation practices.
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