Author:

Keywords:

PSI_VISICS, Science & Technology, Technology, Physical Sciences, Computer Science, Software Engineering, Engineering, Electrical & Electronic, Optics, Computer Science, Engineering, 4006 Communications engineering, 4009 Electronics, sensors and digital hardware, 5102 Atomic, molecular and optical physics

Abstract:

Recently real-time capture of dynamic 3D-objects has become feasible. The dynamic models obtained by various techniques, come in the form of separate highly detailed 3D-meshes with texture at video-rates. These represent such an amount of data, as to hamper manipulation, editing and rendering. Data-compression techniques can alleviate this problem. Independent decimation of the separate meshes, is an inferior solution for what is really time varying mesh. Firstly, it causes unnatural flickering, and secondly, it leaves the inter-mesh correlation unexploited. Therefore, a hybrid technique might be a better solution. It consists of an `intra' compression scheme working on still mesh, a 3D motion estimator/predictor, and a coder for the prediction errors and side information (motion vectors and mesh segmentation). We describe a technique to segment a deforming mesh into regions with locally-uniform motion. We start by interpreting the motion as samples of a 3D vector field. In each point, we estimate the translation, rotation and divergence of the vector field. As human faces are rather incompressible, we ignore the divergence component. Then, we cluster the population with the criterion of similar translation and rotation. Results show that it allows to segment a deforming human face into approximately 200 regions of locally-uniform rigid motion, while keeping the motion prediction error under 5 percent. This is good enough for efficient compression.