Abstract
Text presented in augmented reality (AR) provides in-situ, real-time information for AR users, but its content can be challenging for users to quickly understand on a small head-worn display when engaging in high-cognitive AR tasks. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for AR. Based on a formative study of seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified AR text and text modified via traditional methods. Our work steps towards automating the optimization of batch text data for readability and performance in AR. The target of automatic Video summarization is to create a short skim of the original long video while preserving the major content/events.
Guande Wu (吴冠德)
CS Ph.D. Student
Hi! This is Guande Wu, a Ph.D. student in Tandon School of Engineering, New York University. My advisor is Prof. Claudio T. Silva and I am also working with Prof. Chen Zhao. My research interest mainly lies in the human-AI collaboration especially in AR scenario. Previously, I have worked with many outstanding experts in visualization and software engineering at Zhejiang University, Tongji University, UC Davis and Microsoft Research Asia and Adobe Research.