The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring–dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.