This paper presents a method of pose synthesis based on a low-dimensional space and a set of characteristics of motion learned from examples. This method consists of two phases: learning and synthesis. In the learning phase, a low-dimensional and discrete representation of the space of natural poses is constructed by using a Self Organizing Map (SOM). Meanwhile, a set of matrices is extracted from the motion data. These matrices describe how the poses change with the end-effectors' positions, and play a key role in synthesizing natural looking results. In the synthesis phase, a lightweight algorithm based on the learned parameters is used. The synthesis process is very efficient because there is no time-consuming calculation, like numeric optimization or matrix inverting. Compared with other methods, our method not only can produce natural looking poses in real-time, but also works well with constraints positioned in a larger range. We apply our method in applications of interactive pose editing, real-time motion modification, and pose reconstruction from image. The results have proven the robustness and effectiveness of our method.