Driver posture monitoring is beneficial for identifying driver physical state as well as for optimizing passive safety systems to mitigate injury outcomes during collisions. In recent years, depth cameras are increasingly used to monitor driver’s posture. However, good driver posture data is missing for developing accurate posture recognition methods. In this study, we introduce a method to build an in-vehicle driver posture database for training posture recognition algorithms based on a depth camera. Driver motion data was collected from 23 participants performing both driving and non-driving activities by an optical motion capture system Vicon. Motions were reconstructed by creating personalized digital human skeletons and applying inverse kinematics approach. By taking advantage of the techniques developed in computer graphics, a recorded driver motion can be efficiently retargeted to a variety of virtual humans to build a large database including synthetic depth images, ground truth labels of body segments and skeletal joint centers. Examples from motion reconstruction, data augmentation and preliminary posture prediction results are given.