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A key-pose caching system for rendering an animated crowd in real-time

机译:用于实时渲染动画人群的关键姿势缓存系统

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We present a method to accelerate the visualization of large crowds of animated characters. Linear-blend skinning remains the dominant approach for animating a crowd but its efficiency can be improved by utilizing the temporal and intra-crowd coherencies that are inherent within a populated scene. Our work adopts a caching system that enables a skinned key-pose to be re-used by multi-pass rendering, between multiple agents and across multiple frames. We investigate two different methods; an intermittent caching scheme (whereby each member of a crowd is animated using only its nearest key-pose) and an interpolative approach that enables key-pose blending to be supported. For the latter case, we show that finding the optimal set of key-poses to store is an NP-hard problem and present a greedy algorithm suitable for real-time applications. Both variants deliver a worthwhile performance improvement in comparison to using linear-blend skinning alone.
机译:我们提出了一种方法来加速大量动画角色的可视化。线性混合蒙皮仍然是使人群动画化的主要方法,但是可以通过利用填充场景中固有的时间和人群内相干性来提高其效率。我们的工作采用了一种缓存系统,该系统可使蒙皮的关键姿势在多个代理之间以及在多个框架之间通过多遍渲染来重复使用。我们研究了两种不同的方法;间歇缓存方案(从而仅使用最接近的关键姿势对人群的每个成员进行动画处理)和支持关键姿势混合的插值方法。对于后一种情况,我们表明找到要存储的最佳键位集是一个NP难题,并提出了适用于实时应用的贪婪算法。与仅使用线性混合蒙皮相比,这两种变体均提供了有价值的性能改进。

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