Interactive augmented reality storytelling guided by scene semantics
Changyang Li, Wanwan Li, Haikun Huang, Lap-Fai Yu
Abstract
We present a novel interactive augmented reality (AR) storytelling approach guided by indoor scene semantics. Our approach automatically populates virtual contents in real-world environments to deliver AR stories, which match both the story plots and scene semantics. During the storytelling process, a player can participate as a character in the story. Meanwhile, the behaviors of the virtual characters and the placement of the virtual items adapt to the player's actions. An input raw story is represented as a sequence of events, which contain high-level descriptions of the characters' states, and is converted into a graph representation with automatically supplemented low-level spatial details. Our hierarchical story sampling approach samples realistic character behaviors that fit the story contexts through optimizations; and an animator, which estimates and prioritizes the player's actions, animates the virtual characters to tell the story in AR. Through experiments and a user study, we validated the effectiveness of our approach for AR storytelling in different environments.