融合时空约束的光学动作捕捉标记点实时补全方法  

A Spatio-Temporal Constraints Based Real-Time Optical Motion Capture Missing Marker Recovery Method

作　　者：翁冬冬 王怡晗[1,2] 郭署山 李冬 Weng Dongdong;Wang Yihan;Guo Shushan;Li Dong(Beijing Engineering Research Center of Mixed Reality and Advanced Display,Beijing 100081;School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081;Beijing Near Space Airship Technology Development Co.,LTD,Beijing 100070)

机构地区：[1]北京市混合现实与新型显示工程技术研究中心,北京100081 [2]北京理工大学光电学院,北京100081 [3]北京临近空间飞艇技术开发有限公司,北京100070

出　　处：《计算机辅助设计与图形学学报》2023年第8期1197-1205,共9页Journal of Computer-Aided Design & Computer Graphics

基　　金：广东省重点领域研发计划(2019B010139004);国家自然科学基金(62072036);高等学校学科创新引智计划(B18005);国家国防科技工业局基础科研项目(JCKY2019205A004)。

摘　　要：在基于标记点的光学动作捕捉系统中,针对粘贴在用户身上的标记点受遮挡等因素影响丢失跟踪位置后导致人体位姿计算失败的问题,提出一种基于深度学习的标记点序列预测补全方法.该方法中,深度学习网络模型以人体运动的时间反演对称性作为理论依据,使用双向长短期记忆网络作为网络主体架构;在模型训练过程中提出组合损失函数,分别对人体关键运动节点的活动范围、同一段骨骼上标记点之间的刚性结构,以及标记点运动轨迹的时间连续性进行限制,确保补全的标记点序列符合人体运动的时空约束.在HDM05数据集上的实验结果表明,与现有方法相比,在丢失不同数量、不同时间跨度的标记点序列的条件下,所提方法补全标记点位置的平均误差下降超过14%.In the marker based optical motion capture system,the marker occlusion and various factors can easily lead to a failure of pose reconstruction.This paper proposes a deep learning model based on spatio-temporal constraints for real-time recovery of continuous missing marker sequences.The deep learning network model is based on the time reversal symmetry of human motion and uses the bi-directional long short-term memory network as the backbone of the network.In the process of model training,the combined loss function was proposed to limit the movement range of the key joints,the rigid structure between the markers on the same bone and the time continuity of the markers' movement track,so as to ensure that the recovered marker sequence conforms to the spatio-temporal constraints of human movement.The experimental results on the HDM05 dataset show that the average error of the proposed method is reduced by more than 14% when compared with the existing method,under the condition that different number of marker sequences and different time spans are missing.

关 键 词：标记点补全 光学动作捕捉 时空约束 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]