48,041 research outputs found
Overview of crowd simulation in computer graphics
High-powered technology use computer graphics in education, entertainment, games, simulation, and virtual heritage applications has led it to become an important area of research. In simulation, according to Tecchia et al. (2002), it is important to create an interactive, complex, and realistic virtual world so that the user can have an immersive experience during navigation through the world. As the size and complexity of the environments in the virtual world increased, it becomes more necessary to populate them with peoples, and this is the reason why rendering the crowd in real-time is very crucial. Generally, crowd simulation consists of three important areas. They are realism of behavioral (Thompson and Marchant 1995), high-quality visualization (Dobbyn et al. 2005) and convergence of both areas. Realism of behavioral is mainly used for simple 2D visualizations because most of the attentions are concentrated on simulating the behaviors of the group. High quality visualization is regularly used for movie productions and computer games. It gives intention on producing more convincing visual rather than realism of behaviors. The convergences of both areas are mainly used for application like training systems. In order to make the training system more effective, the element of valid replication of the behaviors and high-quality visualization is added
Developing serious games for cultural heritage: a state-of-the-art review
Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Serious Games in Cultural Heritage
Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201
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