超越平均帧时间：用于XR时序分析的时间序列特征
Beyond Mean Frametime: Time-Series Signatures for XR Timing Analysis
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中文总结 AI 辅助
该研究提出结构感知时间序列特征方法，分析XR时序迹线，通过VR数据集验证其比仅分布摘要更能揭示HMD组间差异，为XR时序分析提供新途径。
中文摘要 AI 辅助
XR系统会暴露诸如运动到光子延迟、帧时间或组件级运行时时序等时序量，这些时序量可作为时间有序的时序迹线被重复观测。传统采用均值、标准差、百分位数或直方图的报告方式虽有用，但会丢失时间顺序。我们提出一种通用的结构感知方法，用于分析和报告XR时序迹线。每条迹线由紧凑、可解释的时间序列特征表示，特征集合可进行可视化和统计比较。我们利用大规模真实VR数据集中的引擎级应用帧时间迹线对该方法进行评估，并比较不同头戴式显示设备（HMD）标签组的时序特征。在多种采样和内容控制条件下，结构感知特征比仅基于分布的摘要更能揭示HMD标签组间显著更强的系统性多变量差异。对单条迹线的时间顺序进行打乱的对照实验会削弱这种分离，尤其在内容匹配条件下，这直接证明原始时间顺序为时序特征贡献了信息。最强的单个特征贡献随采样和内容控制条件而变化，表明在不同分析设置下，没有单一的时序特征占主导地位。尽管该方法在应用帧时间上得到验证，但其表示适用于所有时序迹线，因此为未来应用于其他XR时序量（包括已检测的运动到光子测量）奠定了基础。
英文摘要
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.
