AI 中文总结
本研究提出结合DTW与轨迹插值的运动合成流程，基于FAST VR组装数据集生成100条可区分的XR合成行为轨迹，混合数据集性能与同规模纯真实数据集相当，可用于扩展XR行为群体。
AI 中文摘要
大规模行为数据集对于扩展现实（XR）中的机器学习、个性化和行为建模日益重要。然而，从数百或数千名参与者处收集XR运动数据成本高昂、耗时且难以在不同研究团队间复现，导致许多XR研究仍依赖相对较小的数据集，限制了行为评估的规模与多样性。为解决这一局限，本研究将合成行为群体作为传统XR数据收集的补充方法进行探究，提出一种基于插值的运动合成流程，该流程结合动态时间规整（DTW）与轨迹插值，可从现有XR数据集中生成合成行为轨迹，同时保留任务结构并融入贡献参与者的运动特征。研究使用公开可用的FAST VR组装数据集，生成并公开发布了100条合成行为轨迹，通过基于运动的用户识别对合成轨迹进行评估。结果显示，包含真实与合成轨迹的混合数据集，其性能与同等规模的纯真实数据集相当，且合成轨迹与其贡献参与者间的混淆度较低。与传统数据增强不同，该方法生成可区分的行为轨迹，用于扩展XR行为群体以支持更大规模的行为建模与机器学习评估。研究表明，合成行为群体为扩展XR行为数据集、支持未来数据驱动的沉浸式系统提供了有前景的途径。
英文摘要
Large-scale behavioral datasets are becoming increasingly important for machine learning, personalization, and behavioral modeling in extended reality (XR). However, collecting XR motion data from hundreds or thousands of participants remains expensive, time-consuming, and difficult to reproduce across research groups. As a result, many XR studies continue to rely on relatively small datasets that limit the scale and diversity of behavioral evaluation. To address this limitation, we investigate synthetic behavioral populations as a complementary approach to traditional XR data collection. We present an interpolation-based motion synthesis pipeline that combines dynamic time warping (DTW) with trajectory interpolation to generate synthetic behavioral trajectories from existing XR datasets while preserving task structure and incorporating motion characteristics from contributing participants. Using the publicly available FAST VR assembly dataset, we generated and openly released 100 synthetic behavioral trajectories. We evaluated the synthesized trajectories through motion-based user identification. Hybrid datasets containing both real and synthesized trajectories achieved performance comparable to similarly sized real-only datasets while maintaining low confusion between synthesized trajectories and their contributing participants. Rather than serving as conventional data augmentation, the proposed approach generates distinguishable behavioral trajectories that expand XR behavioral populations for larger-scale behavioral modeling and machine learning evaluation. Our findings demonstrate that synthetic behavioral populations provide a promising approach to expanding XR behavioral datasets and supporting future data-driven immersive systems.
