Fetal motion is an important measure for monitoring fetal health and neurological function. However, current clinical MRI and ultrasound assessments of fetal motion are qualitative and cannot reflect detailed 3D motion of each body part. In this work, we propose a method for fetal motion analysis in MRI using a deep pose estimator. We train a neural network to estimate fetal pose from MR volumes, and extract quantitative metrics of motion from the time series of fetal pose. In the experiments, we study how different conditions affect fetal motion, such as gestational age and maternal position during scan.