Image morphing has proved to be a powerful tool for generating compelling and pleasing visual effects and has been widely used in entertainment industry. However, traditional image morphing methods suffer from a number of drawbacks: feature specification between images is tedious and the reliance on 2D information ignores the possible advantages to be gained from 3D knowledge. In this paper, we utilize recent advantages of computer vision technologies to diminish these drawbacks. By analyzing multi view geometry theories, we propose a processing pipeline based on three reference images. We first seek a few seed correspondences using robust methods and then recover multi view geometries using the seeds, through bundle adjustment. Guided by the recovered two and three view geometries, a novel line matching algorithm across three views is then deduced, through edge growth, line fitting and two and three view geometry constraints. Corresponding lines on a novel image is then obtained by an image transfer method and finally matched lines are fed into the traditional morphing methods and novel images are generated. Novel images generated by this pipeline have advantages over traditional morphing methods: they have an inherent 3D foundation and are therefore physically close to real scenes; not only images located between the baseline connecting two reference image centers, but also extrapolated images away from the baseline are possible; and the whole processing can be either wholly automatic, or at least the tedious task of feature specification in traditional morphing methods can be greatly relieved.