假肢感知的3D人体姿态估计：针对RSP用户的数据集与基准
Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users
- The University of Tokyo（东京大学）
- Sony Computer Science Laboratories（索尼计算机科学实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有方法无法处理假肢几何形状的问题，提出首个RSP用户3D数据集RSP3D，定义假肢感知姿态估计任务，并给出混合基线方法，为后续研究奠定基础。
AI中文摘要：
从视频中恢复3D人体运动对于康复评估和运动表现评价等应用至关重要。对于假肢使用者而言，这需要同时捕捉自然身体关节和假肢装置的几何形状，而现有方法并未针对这一挑战进行设计。基于模型的方法依赖于在非截肢者身上训练的人体模型，无法表示假肢的几何形状，而基于无模型的方法缺乏人体运动学先验，在遮挡情况下不可靠。这一挑战对于使用跑步专用假肢（RSP）的用户尤为突出，因为RSP具有复杂的弯曲几何形状，并在运动过程中动态变化。为填补这一空白，我们收集了RSP3D，这是首个针对RSP用户的3D数据集，涵盖了来自不同截肢状况参与者的基本日常生活和运动动作，使用多相机基于标记的运动捕捉系统进行采集。我们正式定义了假肢感知3D姿态估计的任务，在零样本设置下评估了代表性方法，并确认了它们各自的局限性。我们进一步提出了一种混合基线方法，将基于模型的身体关节估计与基于无模型的RSP形状恢复相结合，为未来研究奠定了起点。
英文摘要：
Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosthesis users, this requires capturing both natural body joints and the geometry of the prosthetic device, a challenge that existing methods are not designed to address. Model-based estimators rely on body models trained on non-amputee individuals and cannot represent prosthesis geometry, while model-free methods lack body kinematic priors and are unreliable under occlusion. This challenge is particularly prominent for users of running-specific prostheses (RSPs), where the RSP has a complex curved geometry and moves dynamically during exercise. To fill this gap, we collect RSP3D, the first 3D dataset of RSP users, covering essential daily-life and exercise actions from participants with varied amputation conditions, using a multi-camera marker-based motion capture setup. We formally define the task of prosthesis-aware 3D pose estimation, evaluate representative methods in a zero-shot setting, and confirm their individual limitations. We further propose a hybrid baseline combining model-based body joint estimation with model-free RSP shape recovery, establishing a starting point for future research.
