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毛涵杨, 李晨, 郭谚霖, 王长波. 动能模型引导的动态虚拟人控制[J]. 计算机辅助设计与图形学学报, 2023, 35(1): 146-154. DOI: 10.3724/SP.J.1089.2023.19270
引用本文: 毛涵杨, 李晨, 郭谚霖, 王长波. 动能模型引导的动态虚拟人控制[J]. 计算机辅助设计与图形学学报, 2023, 35(1): 146-154. DOI: 10.3724/SP.J.1089.2023.19270
MAO Han-yang, LI Chen, GUO Yan-lin, WANG Zhang-bo. Dynamic Virtual Human Control Guided by Kinetic Energy Model[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(1): 146-154. DOI: 10.3724/SP.J.1089.2023.19270
Citation: MAO Han-yang, LI Chen, GUO Yan-lin, WANG Zhang-bo. Dynamic Virtual Human Control Guided by Kinetic Energy Model[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(1): 146-154. DOI: 10.3724/SP.J.1089.2023.19270

动能模型引导的动态虚拟人控制

Dynamic Virtual Human Control Guided by Kinetic Energy Model

  • 摘要: 基于物理的虚拟人控制是一个经典的动力学问题,对游戏开发、影视特效等领域具有重要意义.针对传统的动力学控制器模型构建复杂、缺乏稳定性等问题,提出一个基于动能模型的动态虚拟人控制框架.首先,在黎曼几何空间中对运动捕捉数据进行预处理,构造动能热力分布图;其次,对热力图进行分析,获得控制参数;最后,将得到的参数用于控制器中,计算辅助力矩,使虚拟人保持平衡并提高姿态精度,控制虚拟人进行各种动作.为此,提出一种时间对齐算法,用于融合多个时间片段.通过对复杂地形、运动状态切换和外力作用下的全身双足运动进行测试,获取多种速度、方向的行走数据与跑步数据并进行评估.结果表明,与DeepLoco相比,该框架在外力作用下质心速度变化的波动系数更小,并且能承受更大的干扰力,证明该框架具有鲁棒性.同时,该框架的仿真性能相较DeepLoco提升了2倍,具有高效性.

     

    Abstract: Physically based virtual human control is a typical dynamic problem,which is of great significance to the fields of game development,film special effects,etc.However,traditional dynamic controller model is complex and unstable.To solve this problem,a novel physically-based control framework for dynamic virtual human using kinetic energy model is proposed. Firstly, preprocess reference motion in Riemann geometric space and establish the thermal distribution diagram of kinetic energy. Secondly, obtain the control parameters by analyzing the thermal diagram. Finally, compute restorative torques to rebalance the virtual human and improve the accuracy of posture based on the estimated parameters. In addition, a time alignment algorithm is also presented to integrate multiple reference motion. Simulate the full-body bipedal motion in several situations, including complex terrain, motion transition and external force, to obtain walking and running data of various speeds and directions for evaluation. The results show that, compared with DeepLoco, the proposed framework provides smaller fluctuation coefficients of the change of the center of mass velocity under external force, which demonstrates the robustness. In addition, it achieves 2X performance increase compared to DeepLoco, demonstrating its efficiency.

     

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