自动匹配虚拟人模型与运动数据

使用运动数据驱动虚拟人模型运动是人体运动仿真的常用方法.通常,运动数据本身定义了适合该运动数据的骨架结构,这要求被其驱动的虚拟人模型也必须有相匹配的骨架定义.提出了一种推迟到运动数据导入时再为模型生成骨架结构的基于语义分析的懒匹配算法(lazy match based on semantic analysis,简称LMSA),该算法先用一组平行平面切分人体模型以生成备选关节点集,并在导入运动数据后对备选关节点集和运动数据的骨架结构进行语义分析,匹配具有相同语义的备选关节点和骨架结构的各关节,使已有的虚拟人几何模型能够直接应用于具有不同骨架结构的人体运动数据.

Match the Virtual Human Model and Motion Data Automatically

Captured motion data is widely used in virtual human motion control and synthesis. Usually, the motion data has a native skeleton definition. To apply captured motion on virtual human skin model, the model should have an underlying skeleton that matches the one defined by the motion data. This paper proposes an algorithm called LMSA (lazy match based on semantic analysis) which generates skeleton for existing human model and matches it to the motion data when the motion data is loaded. The LMSA algorithm first generates Candidate-Joint-Set for a human model with a group of parallel planes and then applies the same semantic analysis to both the Candidate-Joint-Set and the skeleton of motion data to match them. By using LMSA algorithm, different motion data can be applied to the existing human model directly without predefining skeleton for human model.