Motion Control Based on Dimensional Reduction and Human Computer Interaction

owing to the high dimension characteristic of motion in catching original data, the high dimensional original data will be projected into low dimensional sub space. This paper applies key frame and dimension reduction method based on several machine learning methods to handle motion capture data. Based on subspace, the human-computer interaction techniques are used to shrink the gap between user’s high-level perception and the low-level features of three-dimensional human motion in the system. After a series of experimental results, our method improves performance of motion retrieval and control.