Xun Qian
Augmented Reality (AR) experiences tightly associate virtual con- tents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR ex- periences in Virtual Reality (VR). First, potential AR consumers col- lect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer au- thors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR expe- rience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR. This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517665 CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; Interactive systems and tools.