開始時刻
前のセッションの直後
83. Immersive Experiences: Creating and Communicating
発表数
5
時間
3分
発表担当
目次
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Elastica: Adaptive Live Augmented Presentations with Elastic Mappings Across Modalities
説明
Augmented presentations offer compelling storytelling by combining speech content, gestural performance, and animated graphics in a congruent manner. The expressiveness of these presentations stems from the harmonious coordination of spoken words and graphic elements, complemented by smooth animations aligned with the presenter's gestures. However, achieving such desired congruence in a live presentation poses significant challenges due to the unpredictability and imprecision inherent in presenters' real-time actions. Existing methods either leveraged rigid mapping without predefined states or required the presenters to conform to predefined animations. We introduce adaptive presentations that dynamically adjust predefined graphic animations to real-time speech and gestures. Our approach leverages script following and motion warping to establish elastic mappings that generate runtime graphic parameters coordinating speech, gesture, and predefined animation state. Our evaluation demonstrated that the proposed adaptive presentation can effectively mitigate undesired visual artifacts caused by performance deviations and enhance the expressiveness of resulting presentations.
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Unlocking Understanding: An Investigation of Multimodal Communication in Virtual Reality Collaboration
説明
Communication in collaboration, especially synchronous, remote communication, is crucial to the success of task-specific goals. Insufficient or excessive forms of communication may lead to detrimental effects on task performance while increasing mental fatigue. However, identifying which combinations of communication modalities provide the most efficient transfer of information in collaborative settings will greatly improve collaboration. To investigate this, we developed a remote, synchronous, asymmetric VR collaborative assembly task application, where users play the role of either mentor or mentee, and were exposed to different combinations of three communication modalities: voice, gestures, and gaze. Through task-based experiments with 25 pairs of participants (50 individuals), we evaluated quantitative and qualitative data and found that gaze did not differ significantly from multiple combinations of communication modalities. Our qualitative results indicate that mentees experienced more difficulty and frustration in completing tasks than mentors, with both types of users preferring all three modalities to be present.
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Meaning Follows Purpose: Unravelling the Architectural Design Conventions in the Contemporary Metaverse
説明
Thousands of people regularly meet, work and play in the architectural spaces that the metaverse offers today. Yet despite the creative potential to disrupt how the built environment is represented, there exists a prevalent belief that the architectural design of the metaverse is rather conventional and reliant on simulating physical reality. We investigated this claim by conducting a design critique study of the most apparent architectural design conventions within the current most popular metaverse platforms, as determined by a scoping review and Google Trends analysis. Based on the opinions of 21 architectural experts on the design of interiors, buildings, and plazas within these platforms, we elicited three overarching design conventions that capture the representation, engagement, and purpose of metaverse architecture. By discussing the impact of these conventions on architectural quality, we inform the future design of metaverse spaces to more purposefully, and perhaps less frequently, use realism to convey meaning.
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LLMR: Real-time Prompting of Interactive Worlds using Large Language Models
説明
We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is scarce, or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. Our framework relies on text interaction and the Unity game engine. By incorporating techniques for scene understanding, task planning, self-debugging, and memory management, LLMR outperforms the standard GPT-4 by 4x in average error rate. We demonstrate LLMR's cross-platform interoperability with several example worlds, and evaluate it on a variety of creation and modification tasks to show that it can produce and edit diverse objects, tools, and scenes. Finally, we conducted a usability study (N=11) with a diverse set that revealed participants had positive experiences with the system and would use it again.
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Using the Visual Language of Comics to Alter Sensations in Augmented Reality
説明
Augmented Reality (AR) excels at altering what we see but non-visual sensations are difficult to augment. To augment non-visual sensations in AR, we draw on the visual language of comic books. Synthesizing comic studies, we create a design space describing how to use comic elements (e.g., onomatopoeia) to depict non-visual sensations (e.g., hearing). To demonstrate this design space, we built eight demos, such as speed lines to make a user think they are faster and smell lines to make a scent seem stronger. We evaluate these elements in a qualitative user study (N=20) where participants performed everyday tasks with comic elements added as augmentations. All participants stated feeling a change in perception for at least one sensation, with perceived changes detected by between four participants (touch) and 15 participants (hearing). The elements also had positive effects on emotion and user experience, even when participants did not feel changes in perception.
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