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SAUCE: Asset Libraries of the Future
... 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. ...
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. ...
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.
In modern society, the increasing number of web search operations on various search engines has become ubiquitous due to the significant number of results presented to the users and the incompetent result-ranking mechanism in some domains, such as medical, law, and academia. As a result, the user is overwhelmed with a large number of misranked or uncategorized search results. One of the most promising technologies to reduce the number of results and provide desirable information to the users is dynamic faceted filters. Therefore, this paper extensively reviews related research articles published in IEEE Xplore, Web of Science, and the ACM digital library. As a result, a total of 170 related research papers were considered and organized into five categories. The main contribution of this paper is to provide a detailed analysis of the faceted search’s fundamental attributes, as well as to demonstrate the motivation from the usage, concerns, challenges, and recommendations to enhance the use of the faceted approach among web search service providers.
Recent years has seen an explosion in Machine Learning (ML) research. The challenge is now to transfer these new algorithms into the hands of artists and TD's in visual effects and animation studios, so that they can start experimenting with ML within their existing pipelines. This paper presents some of the current challenges to experimentation and deployment of ML frameworks in the post-production industry. It introduces our open-source "ML-Server" client / server system as an answer to enabling rapid prototyping, experimentation and development of ML models in post-production software. Data, code and examples for the system can be found on the GitHub repository page:
https://github.com/TheFoundryVisionmongers/nuke-ML-server
Machine learning techniques are not often associated with artistic work such as visual effects production. Nevertheless, these techniques can save a lot of time for artists when used in the right context. In recent years, deep learning techniques have become a widely used tool with powerful frameworks that can be employed in a production environment. We present two deep learning solutions that were integrated into our production pipeline and used in current productions. One method generates high quality images from a compressed video file that contains various compression artifacts. The other quickly locates slates and color charts used for grading in a large set of images. We discuss these particular solutions in the context of previous work, as well as the challenges of integrating a deep learning solution within a VFX production pipeline, from concept to implementation.
Universality, the property of the Web that makes it the largest data and information source in the world, is also the property behind the lack of a uniform organization scheme that would allow easy access to data and information. A semantic web, wherein different applications and Web sites can exchange information and hence exploit Web data and information to their full potential, requires the information about Web resources to be represented in a detailed and structured manner. Resource Description Framework (RDF), an effort in this direction supported by the World Wide Web Consortium, provides a means for the description of metadata which is a necessity for the next generation of interoperable Web applications. The success of RDF and the semantic web will depend on (1) the development of applications that prove the applicability of the concept, (2) the availability of application interfaces which enable the development of such applications, and (3) databases and inference systems that exploit RDF to identify and locate most relevant Web resources. In addition, many practical issues, such as security, ease of use, and compatibility, will be crucial in the success of RDF. This survey aims at providing a glimpse at the past, present, and future of this upcoming technology and highlights why we believe that the next generation of the Web will be more organized, informative, searchable, accessible, and, most importantly, useful. It is expected that knowledge discovery and data mining can benefit from RDF and the Semantic Web.
Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet ¹ provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
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