Animals use their whole body to navigate their environment and solve tasks. Zebrafish (Dario rerio) need to coordinate tail and fin movements and can serve as a model organism for the study of locomotion and locomotion generation and dysregulation. Behavioral tracking of zebrafish has usually restricted itself to tracking in the (x,y) plane with an overhead camera. This precludes studies that investigate postural control or navigation in naturalistic, three-dimensional conditions. To study full body coordination we built a rig that allows us to track larval zebrafish in 3D with high resolution while stimuli are being presented. Subsequently, key-points and features are extracted from videos using the neural network based software DeepLabCut as well as custom python scripts. We identify movement patterns of fins together with tail based clustering and aim to ultimately build a whole locomotion atlas of the larval zebrafish. This setup can shed light on networks for motion generation and provide the basis to study zebrafish behavior with unprecedented detail.