An Extendable Multiagent Model for Behavioural Animation

This paper presents a framework for visually simulating the behaviour of actors in virtual environments. In principle, the environmental interaction follows a cyclic processing of perception, decision, and action. As natural life-forms perceive their environment by active sensing, our approach also tends to let the artificial actor actively sense the virtual world. This allows us to place the characters in non-preprocessed virtual dynamic environments, what we call generic environments. A main aspect within our framework is the strict distinction between a behaviour pattern, that we term model, and its instances, named characters, which use the pattern. This allows them sharing one or more behaviour models. Low-level tasks like sensing or acting are took over by so called subagents, which are subordinated modules extendedly plugged in the character. In a demonstration we exemplarily show the application of our framework. We place the same character in different environments and let it climb and descend stairs, ramps and hills autonomously. Additionally the reactiveness for moving objects is tested. In future, this approach shall go into action for a simulation of an urban environment

Open Medical Gesture: An Open-Source Experiment in Naturalistic Physical Interactions for Mixed and Virtual Reality Simulations

Mixed Reality (MR) and Virtual Reality (VR) simulations are hampered by requirements for hand controllers or attempts to perseverate in use of two-dimensional computer interface paradigms from the 1980s. From our efforts to produce more naturalistic interactions for combat medic training for the military, USC has developed an open-source toolkit that enables direct hand controlled responsive interactions that is sensor independent and can function with depth sensing cameras, webcams or sensory gloves. Natural approaches we have examined include the ability to manipulate virtual smart objects in a similar manner to how they are used in the real world. From this research and review of current literature, we have discerned several best approaches for hand-based human computer interactions which provide intuitive, responsive, useful, and low frustration experiences for VR users.Comment: AHFE 202

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

Virtual human representation and communication in VLNet

[No abstract available