In this study, we present an efficient approach to recover 3D human motion from monocular image sequences in generative reconstruction framework. This approach is based on the extracting of motion base space. From the motion capture data with bothersome high dimension characteristic of human activity, we extract the motion base space in which human pose can be described essentially and concisely by a more controllable way. And then, the structure of this space corresponding to some special activities such as walking motion is explored with data clustering. For the single image, Gaussian mixture model is used to generate the candidates of 3D pose. The shape context is the common descriptor of image silhouette feature and synthetical feature of human model. We get the shortlist of 3D poses by measuring the shape contexts matching cost between image feature and the synthetical features. In tracking situation, an AR model trained by the example sequences produces almost accurate pose predictions. Experiments demonstrate that the proposed approach works well.