Over the past decades, there has been an increasing interest in spine kinematics. Various approaches have been proposed on how to observe and analyse spine kinematics from a computer vision perspective. Amongst all, emphasis has been given to both the shape of the individual vertebrae as well as the overall spine curvature as a means of providing accurate and valid spinal condition diagnosis. Traditional invasive methods cannot accurately delineate the intersegmental motion of the spine vertebrae. On the contrary, capturing and measuring spinal motion via the non-invasive fluoroscopy has been a popular technique choice because of its low incurred patient radiation exposure nature. In general, image-based and other reconstruction methods target individual frames and focus on static spine instances. However, even the ones analysing sequences yield in unstable and jittery animations of the reconstructed spine. In this report, we address this issue using a novel approach to robustly reconstruct and rigidly derive a shape with no inter-frame variations. This is to produce animations that are jitter free across our sequence based on fluoroscopy video. Our main contributions are 1) retaining the shape of the solid vertebrae across the frame range, 2) helping towards a more accurate image segmentation even when there's a limited training set. We show our pipeline's success by reconstructing and comparing 3D animations of the lumbar spine from a corresponding fluoroscopic video
