We present a novel framework that consists of two-level regressors for finding correlations between human shapes and landmark positions in both body part and holistic scales. To this end, we first develop pose invariant coordinates of landmarks that represent both local and global shape features by using the pose invariant local shape descriptors and their spatial relationships. Our body part-level regression deals with the shape features from only those body parts corresponding to a certain landmark. For this, we develop a method that identifies such body parts per landmark, by using geometric shape dictionary obtained through the bag of features method. Our method is nearly automatic, requiring human assistance only once to dierentiate the left and right sides, and shows the prediction accuracy comparable to or better than those of existing methods, with a test data set containing a large variation of human shapes and poses.