Feature Aware Multiresolution Animation Models Generation

In computer graphics, investigation has been made in many years in LOD (Level of Detail) models generation to alleviate the workload of visualization processing. However, most of the existing algorithms only focus on static models, and very little works are to deal with animation models. In this paper, we propose a feature aware method for generating multiresolution animation models. By the method we first use a normal deviation based quadric error metric to generate a base hierarchy, and then update the edge contraction order progressively from the first frame to the last one. Thus, we can get the simplified versions for each frame in the animation sequence. Finally, a mesh optimization method is proposed to allow visual distortion of the simplified models to be further alleviated. We demonstrate that our method works efficiently and can generate better approximations with well-preserved features for each frame than previous methods.