基于局部姿态先验的深度图像3D人体运动捕获方法

提出一种基于局部姿态先验的从深度图像中实时在线捕获3D人体运动的方法.关键思路是根据从捕获的深度图像中自动提取具有语义信息的虚拟稀疏3D标记点，从事先建立的异构3D人体姿态数据库中快速检索K个姿态近邻并构建局部姿态先验模型，通过迭代优化求解最大后验概率，实时地在线重建3D人体姿态序列.实验结果表明，该方法能够实时跟踪重建出稳定、准确的3D人体运动姿态序列，并且只需经过个体化人体参数自动标定过程，可跟踪身材尺寸差异较大的不同表演者；帧率约25fps.因此，所提方法可应用于3D游戏/电影制作、人机交互控制等领域.

Local Pose Prior Based 3D Human Motion Capture from Depth Camera

This paper introduces a local pose prior based real-time online approach to capture 3D human animation from a single depth camera. The key idea is to learn a series of local pose prior models with K motion capture examples from a pre-established large and heterogeneous human motion database, based on automatically extracted labelled virtual sparse 3D markers from captured depth image. Then, by solving a Maximum A Posterior (MAP) problem via an iteratively optimization process, the system automatically tracks the 3D human motion sequence. The experiments show that the proposed approach robustly captures the accurate 3D human motions at 25fps. The proposed tracking system can easily applied to different actors with large different body sizes via an automatically individual body parameters calibration process. The proposed system can widely apply to 3D game/movie produce, human-machine interaction.