Recent advancements in visual-inertial motion capture systems have demonstrated the potential of combining monocular cameras with sparse inertial measurement units (IMUs) as cost-effective solutions, which effectively mitigate occlusion and drift issues inherent in single-modality systems. However, they are still limited by metric inaccuracies in global translations stemming from monocular depth ambiguity, and shape-agnostic local motion estimations that ignore anthropometric variations. We present Stereo-Inertial Poser, a real-time motion capture system that leverages a single stereo camera and six IMUs to estimate metric-accurate and shape-aware 3D human motion. By replacing the monocular RGB with stereo vision, our system resolves depth ambiguity through calibrated baseline geometry, enabling direct 3D keypoint extraction and body shape parameter estimation. IMU data and visual cues are fused for predicting drift-compensated joint positions and root movements, while a novel shape-aware fusion module dynamically harmonizes anthropometry variations with global translations. Our end-to-end pipeline achieves over 200 FPS without optimization-based post-processing, enabling real-time deployment. Quantitative evaluations across various datasets demonstrate state-of-the-art performance. Qualitative results show our method produces drift-free global translation under a long recording time and reduces foot-skating effects.

翻译：近年来，视觉-惯性运动捕捉系统的进展展示了将单目相机与稀疏惯性测量单元（IMU）结合作为经济高效解决方案的潜力，这类系统能有效缓解单模态系统固有的遮挡与漂移问题。然而，它们仍受限于单目深度模糊导致的全局平移度量不精确，以及忽略人体测量学差异的形状无关局部运动估计。本文提出立体惯性姿态估计器（Stereo-Inertial Poser），一种利用单目立体相机与六个IMU实时估计度量精确且形状感知的三维人体运动的系统。通过用立体视觉替代单目RGB，我们的系统借助标定基线几何解析深度模糊，实现直接的三维关键点提取与身体形状参数估计。IMU数据与视觉线索融合用于预测漂移补偿的关节位置与根节点运动，同时新颖的形状感知融合模块动态协调人体测量学差异与全局平移。我们的端到端流程无需基于优化的后处理即可实现超过200 FPS，支持实时部署。跨多个数据集的定量评估证明了其领先性能。定性结果表明，我们的方法在长时间录制下能产生无漂移的全局平移，并减少脚部滑动效应。