School of Engineering, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343, USA.
IEEE Trans Vis Comput Graph. 2013 May;19(5):774-86. doi: 10.1109/TVCG.2012.149.
We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.
我们提出了基于特征的运动图，用于在障碍物之间进行逼真的运动合成。与传统的运动图相比，基于特征的运动图具有多个优点，例如提高了搜索查询的效果、消除了脚部滑动去除的后处理需求以及减少了计算需求。我们的贡献有三点。首先，我们表明基于相关特征选择转换可以显著减少图构建时间，并提高搜索性能。其次，我们采用了一种快速通道搜索方法，将运动图搜索限制在具有障碍物之间保证净空的自由通道中，从而实现更快、更优的结果，避免了昂贵的碰撞检查。最后，我们提出了一种基于反向运动学的运动变形模型，应用于解决方案分支的转换。为每个转换分配一个连续的变形范围，该范围不超过用户为图构建指定的原始转换成本阈值。所得到的变形提高了基于特征的运动图的可达性，从而也减少了搜索过程中的时间消耗。所提出方法的结果进行了评估和量化，与传统的运动图技术相比，这些结果显示出了显著的改进。
