To date, in-vehicle posture datasets based on image synthesis has attracted increased attention. However, few of them showed effectiveness and reusability on in-vehicle posture estimation due to lack of natural posture variations. This work therefore is dedicated to test the potential of a well-designed synthetic dataset for driver posture estimation. The findings of this work imply that a well-designed synthetic posture dataset is of potential for developing state-of-the-art in-vehicle posture estimation models, leading to better understanding of driver behavior. As the data generation method proposed in this work can be easily adapted given the configurations of any in-car cameras, relevant datasets can be established at basically no cost, without bothering to collect annotations for a real dataset which is expensive and time consuming.