Video motion magnification forms a relatively novel family of visualization techniques, that aim to magnify imperceivably small motions in videos. The most prominent techniques are based on Eulerian video processing and local phase shifting, which modify pixel time courses, rather than relying on explicit motion estimation.In this work, we show that under ideal conditions in the context of psychophysiological experiments, a Lagrangian motion magnification approach based on dense optical flow estimation, can be superior to Eulerian motion magnification strategies. We present a novel, continuous and motion magnitude driven forward warping scheme of small motions, which implements motion compensation and magnification into a single motion estimation step. Our approach does not rely on temporal filtering and works in the presence of large motion. It does not require the explicit identification of fast moving objects and more generally no segmentation and or matting in the image domain is necessary. We apply our method to the visualization of blinking related modulations in micro-saccadic eye movements ((i.a.. iridodonesis), pupil dilation (hippus) and micro-expression analysis.