Beyond Mean Frametime: Time-Series Signatures for XR Timing Analysis

2026-08-21

AI Summary

本文提出一种结构感知的方法来分析XR系统的时序特征，通过时间序列签名保留了时序信息，并在大规模VR数据集上验证了其有效性。

Abstract

XR systems expose timing quantities, such as motion-to-photon latency, frametime, or component-level runtime timings, that can be observed repeatedly as temporally ordered timing traces. Conventional reporting with means, standard deviations, percentiles, or histograms is useful, but it discards temporal ordering. We propose a general structure-aware methodology for analyzing and reporting XR timing traces. Each trace is represented by a compact, interpretable time-series signature, and collections of signatures can be visualized and compared statistically. We evaluate the method using engine-level application frametime traces from a large-scale in-the-wild VR dataset and compare timing signatures across HMD-labelled groups. Across multiple sampling and content-control conditions, structure-aware signatures reveal substantially stronger systematic multivariate differences between HMD-labelled groups than distribution-only summaries. A within-trace temporal-order shuffle control reduces this separation, particularly under content matching, providing direct evidence that original temporal ordering contributes information to the timing signatures. The strongest individual feature contributions vary across sampling and content-control conditions, indicating that no single timing characteristic dominates across analysis settings. Although demonstrated on application frametime, the representation operates on timing traces and therefore provides a basis for future application to other XR timing quantities, including instrumented motion-to-photon measurements.

Problem

• XR timing analysis
• temporal ordering
• time-series signatures

Innovation

• structure-aware methodology
• time-series signature
• XR timing analysis
• temporal ordering