BOXR: Body and head motion Optimization framework for eXtended Reality

The emergence of standalone Extended Reality (XR) systems has enhanced user mobility, accommodating both subtle, frequent head motions and substantial, less frequent body motions. However, the pervasively used Motion-to-Display (M2D) latency metric, which measures the delay between the most recent motion and its corresponding display update, only accounts for head motions. This oversight can leave users prone to motion sickness if significant body motion is involved. Although existing methods optimize M2D latency through asynchronous task scheduling and reprojection methods, they introduce challenges like resource contention between tasks and outdated pose data. These challenges are further complicated by user motion dynamics and scene changes during runtime. To address these issues, we for the first time introduce the Camera-to-Display (C2D) latency metric, which captures the delay caused by body motions, and present BOXR, a framework designed to co-optimize both body and head motion delays within an XR system. BOXR enhances the coordination between M2D and C2D latencies by efficiently scheduling tasks to avoid contentions while maintaining an up-to-date pose in the output frame. Moreover, BOXR incorporates a motion-driven visual inertial odometer to adjust to user motion dynamics and employs scene-dependent foveated rendering to manage changes in the scene effectively. Our evaluations show that BOXR significantly outperforms state-of-the-art solutions in 11 EuRoC MAV datasets across 4 XR applications across 3 hardware platforms. In controlled motion and scene settings, BOXR reduces M2D and C2D latencies by up to $63 \%$ and $27 \%$, respectively and increases frame rate by up to $43 \%$. In practical deployments, BOXR achieves substantial reductions in real-world scenarios-up to $42 \%$ in M2D latency and $31 \%$ in C2D latency-while maintaining remarkably low miss rates of only $1.6 \%$ for M2D requirements and $\mathbf{1. 0 \%}$ for C2D requirements.

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Publication Details

Published
2024-12-10
DOI
https://doi.org/10.1109/rtss62706.2024.00016
Citations
3
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
0.75
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article

BOXR: Body and head motion Optimization framework for eXtended Reality

Zexin Li, Hyoseung Kim, Cong Liu, Ziliang Zhang
3 citations
Augmented Reality Applications
0.75
article

BOXR: Body and head motion Optimization framework for eXtended Reality

Zexin Li, Hyoseung Kim, Cong Liu, Ziliang Zhang
article en
3 citations

Abstract

The emergence of standalone Extended Reality (XR) systems has enhanced user mobility, accommodating both subtle, frequent head motions and substantial, less frequent body motions. However, the pervasively used Motion-to-Display (M2D) latency metric, which measures the delay between the most recent motion and its corresponding display update, only accounts for head motions. This oversight can leave users prone to motion sickness if significant body motion is involved. Although existing methods optimize M2D latency through asynchronous task scheduling and reprojection methods, they introduce challenges like resource contention between tasks and outdated pose data. These challenges are further complicated by user motion dynamics and scene changes during runtime. To address these issues, we for the first time introduce the Camera-to-Display (C2D) latency metric, which captures the delay caused by body motions, and present BOXR, a framework designed to co-optimize both body and head motion delays within an XR system. BOXR enhances the coordination between M2D and C2D latencies by efficiently scheduling tasks to avoid contentions while maintaining an up-to-date pose in the output frame. Moreover, BOXR incorporates a motion-driven visual inertial odometer to adjust to user motion dynamics and employs scene-dependent foveated rendering to manage changes in the scene effectively. Our evaluations show that BOXR significantly outperforms state-of-the-art solutions in 11 EuRoC MAV datasets across 4 XR applications across 3 hardware platforms. In controlled motion and scene settings, BOXR reduces M2D and C2D latencies by up to $63 \%$ and $27 \%$, respectively and increases frame rate by up to $43 \%$. In practical deployments, BOXR achieves substantial reductions in real-world scenarios-up to $42 \%$ in M2D latency and $31 \%$ in C2D latency-while maintaining remarkably low miss rates of only $1.6 \%$ for M2D requirements and $\mathbf{1. 0 \%}$ for C2D requirements.

University of California, Riverside (US)
Openalex Percentile: Top 26%
Augmented Reality Applications
0.75
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BOXR: Body and head motion Optimization framework for eXtended Reality — Zexin Li, Hyoseung Kim, et al. · (2024) | TGRS Research Map | TGRS