An accurate and personalized full 3D body shape in a position of interest is needed for many applications such as textile industry for specifying product size. Though a full body surface can be easily scanned using a body scanner, raw scans are generally noisy, incomplete, and require a more or less time consuming post processing to obtain a workable surface model. In recent years, thanks to the development of statistical body shape models (SBSM), researchers used these models to remove noise, complete holes making it possible to create high quality surface models even from low cost depth cameras. Similarly, the use of SBSM also offers the possibility to generate a realistic full body shape with a limited number of measures/predictors such as traditional anthropometric dimensions, surface landmarks etc… The purpose of the present work is to explore the possibility to create a personalized surface model with a small set of easily measurable parameters, and to compare the quality of SBSM-based prediction in function of predictors. A sample of 164 full body scans in a standing posture from European and Chinese males were selected based on stature and BMI. After cleaning the raw scans, a non-rigid mesh deformation method was used to registrer a customized template onto scans. Then, a principal component analysis (PCA) was performed to build SBSM with different set of predictors, including anthropometric dimensions, landmarks’ position, postural parameters. The partial least square regression was used to take into account correlated predictors. As statistical models cannot match the target values of predictors, an optimization was further proposed for better matching targets while not deviating too much from the initial prediction by statistical regression. A leave-one-out (LOO) procedure was used to evaluate the quality of SBSM with different set of predictors.