• Haiyu Wang, New York University


• Wenxuan Liu, New York University


• Kenneth Chen, New York University


• Qi Sun, New York University


• Sai Qian Zhang, New York University

 

Abstract

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Virtual reality (VR) plays a crucial role in advancing immersive, interactive experiences that transform learning, work, and entertainment by enhancing user engagement and expanding possibilities across various fields. Image rendering is one of the most crucial application in VR, as it produces high-quality, realistic visuals that are vital for maintaining immersive user experiences and preventing visual discomfort or motion sickness. However, the cost of image rendering in VR environment is considerable, primarily due to the demands of high-quality visual experiences from users. This challenge is even greater in real-time applications, where maintaining low latency further increases the complexity of the rendering process. On the other hand, VR devices, such as head-mounted displays (HMDs), are intrinsically linked to human behavior, using insights from perception and cognition to enhance user experience.

In this work, we aim to reduce the high computational costs of the rendering process in VR by leveraging natural human eye dynamics and focusing on processing only where you look (POLO). This involves co-optimizing AI algorithms with underlying hardware for greater efficiency. We introduce POLONet, an efficient multitask deep learning framework designed to track human eye movements with minimal latency. Integrated with the POLO accelerator as a plug-in for VR HMD SoCs, this approach significantly lowers image rendering costs, achieving up to a 3.9x reduction in end-to-end latency compared to the latest gaze tracking methods.