%0 Journal Article
%T Reconstructing 3D Tree Models Using Motion Capture and Particle Flow
%A Jie Long
%A Michael D. Jones
%J International Journal of Computer Games Technology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/363160
%X Recovering tree shape from motion capture data is a first step toward efficient and accurate animation of trees in wind using motion capture data. Existing algorithms for generating models of tree branching structures for image synthesis in computer graphics are not adapted to the unique data set provided by motion capture. We present a method for tree shape reconstruction using particle flow on input data obtained from a passive optical motion capture system. Initial branch tip positions are estimated from averaged and smoothed motion capture data. Branch tips, as particles, are also generated within a bounding space defined by a stack of bounding boxes or a convex hull. The particle flow, starting at branch tips within the bounding volume under forces, creates tree branches. The forces are composed of gravity, internal force, and external force. The resulting shapes are realistic and similar to the original tree crown shape. Several tunable parameters provide control over branch shape and arrangement. 1. Introduction Reconstruction of tree shape from motion capture data is an important step in replaying motion capture of trees under external forces, such as natural wind. Motion capture provides a fast and easy way to collect the locations over time of retroreflective marker locations placed on an object. In this paper, we address the problem of creating 3D tree shape from motion capture data. We also discuss the best practices for collecting motion data from a tree. This research will focus on reconstructing static 3D tree shape with branching skeletons from data collected by motion capture system. Solutions to the motion capture problem for trees can be applied to problems in visual effects and the study of tree motion. Motion capture is a potential solution because motion capture data includes wind effects, which are difficult to model in simulation, such as variable branch stiffness, nonuniform variation in size, and emergent effects due to leaf deformation. Tree shape modeling has long been studied on computer graphics. L-systems [1¨C3] generate branching structures using predefined rules. Parametric models [4] embed tree¡¯s biology information into growth and shape using a parametric set. Approaches based on photographs [5¨C7] or videos [8, 9] create tree shapes in 3D space by image processing methods. Laser scanning has been employed for collecting information of 3D tree shapes [10¨C12]. Particle systems [6, 13¨C15] represent each branch as a result from particle flow simulation. Most of these methods result in satisfying tree shapes but do not
%U http://www.hindawi.com/journals/ijcgt/2013/363160/