Surgery simulation is a growing field of research comprising the efforts of various disciplines including Computer Graphics, Computer Vision, Medicine, Mechanics, Robotics and even Animation. We try to combine, adapt and extended different solutions to this problem and re-assemble them using mid-range 3D graphics hardware, modern object-oriented methods and free visualization toolkits. Reconstructions of the physical based realistic 3D models are achieved form CT scans. In particular, we focus on the generic model concept, an evolving methods to encapsulate generic information in a generic 3D mesh. This model is subsequently deformed using multivariate scattered data interpolation technique show that it matches the reconstructed model of the patient being studied, under the control of common landmark points. We describe a wy to build a topological relationship between these non-related geometrical and topological models, thus opening a framework to transfer different kinds of generic information, ranging from algorithmic simplification and computation hints to anatomical features or learning material. Starting from several uncalibrated photographs, we also show how 2D features points may be used to recover the camera parameters and employ them as control points to deformed our model in a similar fashion, so that the texture information is retrieved after projecting the mode into the photographic pose.