Browsing by Author "Kaufmann, Hannes"

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    Embodied Conversational Agents with Situation Awareness for Training in Virtual Reality
    (The Eurographics Association, 2023) Kán, Peter; Rumpelnik, Martin; Kaufmann, Hannes; Jean-Marie Normand; Maki Sugimoto; Veronica Sundstedt
    Embodied conversational agents have a great potential in virtual reality training applications. This paper investigates the impact of conversational agents on users in a first responder training scenario. We integrated methods for automatic speech recognition and speech synthesis with natural language processing into a VR training application in the Unity game engine. Additionally, we present a method for enabling situation awareness for agents in a virtual environment. Finally, we conducted a between-subject lab experiment with 24 participants which investigated differences between conversational agents and agents with pre-scripted audio. Several metrics were measured in the experiment including presence, subjective task performance, learning outcome, interaction quality, quality of information presentation, perceived realism, co-presence, and training task duration. Our results suggest that users trying our conversational agents condition experienced significantly higher level of copresence than users with pre-scripted audio. Additionally, significant differences in subjective task performance and training duration were discovered between genders. Based on the results of our qualitative analysis, we provide guidelines that can facilitate future design of VR training applications and research studies with embodied conversational agents.
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    Ownership Estimation for Tracked Hands in a Colocated VR Environment
    (The Eurographics Association, 2023) Reimer, Dennis; Scherzer, Daniel; Kaufmann, Hannes; Jean-Marie Normand; Maki Sugimoto; Veronica Sundstedt
    Hand tracking systems play a crucial role in virtual reality (VR) applications, typically focusing on tracking the hands of the user who is using the system. Consequently, most existing systems are designed to track a maximum of two hands simultaneously. However, in certain colocated multi-user VR scenarios, it becomes necessary to track more than two hands simultaneously, such as to eliminate blind spots in individual tracking systems. In such scenarios, accurately assigning the tracked hands to the corresponding users using only the hand locations relative to the users becomes essential. This paper introduces and evaluates various methods for efficiently assigning hands to users in such scenarios. Additionally, we propose an algorithm that leverages past assignments to enhance the robustness and effectiveness of future assignments. Our experimental results demonstrate that this algorithm significantly improves upon existing methods. Furthermore, when combined with an assignment algorithm based on reinforcement learning AI agents, we achieve a remarkable 99% accuracy in hand assignments. As a result, we present an assignment algorithm specifically tailored for colocated VR scenarios, utilizing only the hand and user locations within the scene, making it directly applicable in the aforementioned contexts.