Precomputing data-driven tree animation

摘要

We present a data-driven approach that synthesizes tree animations from a set of pre-computed motion data. Our approach improves previous motion synthesis algorithms for character animation in several aspects. We first introduce a simple yet effective sampling scheme to generate a rich and reusable motion database for each tree model. We also propose a novel technique to generate a fine set of transitions that are uniformly distributed in the motion database. The transition lengths are adaptively determined according to the similarity of the transiting frame pairs. In the runtime, we employ a greedy searching algorithm to synthesize smooth tree animations under an adjustable wind condition. Experimental results show that our approach achieves comparable quality to physically based methods, while in orders of magnitude faster performance.