CARING-AI combines ChatGPT text generation, environment scanning, and smoothed text-to-motion diffusion to let authors create spatially grounded AR avatar instructions without coding or motion capture.
Design Patterns for Situated Visualization in Augmented Reality
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abstract
Situated visualization has become an increasingly popular research area in the visualization community, fueled by advancements in augmented reality (AR) technology and immersive analytics. Visualizing data in spatial proximity to their physical referents affords new design opportunities and considerations not present in traditional visualization, which researchers are now beginning to explore. However, the AR research community has an extensive history of designing graphics that are displayed in highly physical contexts. In this work, we leverage the richness of AR research and apply it to situated visualization. We derive design patterns which summarize common approaches of visualizing data in situ. The design patterns are based on a survey of 293 papers published in the AR and visualization communities, as well as our own expertise. We discuss design dimensions that help to describe both our patterns and previous work in the literature. This discussion is accompanied by several guidelines which explain how to apply the patterns given the constraints imposed by the real world. We conclude by discussing future research directions that will help establish a complete understanding of the design of situated visualization, including the role of interactivity, tasks, and workflows.
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cs.HC 1years
2025 1verdicts
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CARING-AI: Towards Authoring Context-aware Augmented Reality INstruction through Generative Artificial Intelligence
CARING-AI combines ChatGPT text generation, environment scanning, and smoothed text-to-motion diffusion to let authors create spatially grounded AR avatar instructions without coding or motion capture.