Xun Qian
Purdue University, West Lafayette, Indiana, United States
Augmented Reality (AR) experiences tightly associate virtual contents 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 experiences in Virtual Reality (VR). First, potential AR consumers collect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer authors 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 experience 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.
https://dl.acm.org/doi/abs/10.1145/3491102.3517665
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)