We introduce a multi-view stereoscopic video database with a green screen, called MTF, for the usages in computer vision applications, in particular for free navigation, free-viewpoint television, and video transition quality-of-experience (QoE) assessment. The MTF contains full-HD videos of real storytelling made up of 3 scenes. One particularity of this dataset is that to understand its storytelling, users must change their point of view in the scene at a given time. To this end, we usually need to generate a transition to link two points of view in the same scene. Computer vision techniques that enable such transitions like view synthesis methods, rely on a set of images of the scene to render some new views from different viewpoints of this scene. However, these methods may have many failure cases that lead to artifacts in the final rendered video transition. In most view synthesis QoE tests, the contents are not designed to make the transition between two points of view useful or interesting for the viewers, e.g. they don't need to make a transition to capture more information to better understand the content. We thus, assume that participants will harshly judge artifacts and imperfections in the rendered transition. Thus, the MTF is expected to enable a better analysis of the visual impact of persistent artifacts in the final rendered transition. In our dataset, all the scenes are recorded in a green screen studio, which is often used to superimpose special effects and scenery during editing according to specific needs. Our dataset also presents a wide baseline camera-setup, a challenging constraint for view synthesis techniques. Finally, The MTF can also be used as a complementary dataset with others in literature in various computer vision applications, such as video compression, 3D video content, immersive virtual reality environment, optical flow estimation ...