Analytic simplification of animated characters

Bruce Merry, P. Marais, J. Gain
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引用次数: 2

Abstract

Traditionally, levels of detail (LOD) for animated characters are computed from a single pose. Later techniques refined this approach by considering a set of sample poses and evaluating a more representative error metric. A recent approach to the character animation problem, animation space, provides a framework for measuring error analytically. The work presented here uses the animation-space framework to derive two new techniques to improve the quality of LOD approximations. Firstly, we use an animation-space distance metric within a progressive mesh-based LOD scheme, giving results that are reasonable across a range of poses, without requiring that the pose space be sampled. Secondly, we simplify individual vertices by reducing the number of bones that influence them, using a constrained least-squares optimisation. This influence simplification is combined with the progressive mesh to form a single stream of simplifications. Influence simplification reduces the geometric error by up to an order of magnitude, and allows models to be simplified further than is possible with only a progressive mesh. Quantitative (geometric error metrics) and qualititative (user perceptual) experiements confirm that these new extensions provide significant improvements in quality over traditional, naïve simplification; and while there is naturally some impact on the speed of the off-line simplification process, it is not prohibitive.
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动画人物的分析简化
传统上,动画人物的细节级别(LOD)是从单个姿势计算的。后来的技术通过考虑一组样本姿势和评估更具代表性的误差度量来改进这种方法。最近的一种解决角色动画问题的方法,动画空间,提供了一个分析测量误差的框架。本文介绍的工作使用动画空间框架来推导两种新技术来提高LOD近似的质量。首先,我们在基于渐进式网格的LOD方案中使用动画空间距离度量,在不需要对姿态空间进行采样的情况下,在一系列姿态范围内给出合理的结果。其次,我们使用约束最小二乘优化,通过减少影响它们的骨骼数量来简化单个顶点。这种影响简化与渐进式网格相结合,形成单一的简化流。影响简化将几何误差减少到一个数量级,并允许模型比仅使用渐进式网格进一步简化。定量(几何误差度量)和定性(用户感知)实验证实,这些新的扩展在质量上比传统的naïve简化提供了显著的改进;虽然离线简化过程的速度自然会受到一些影响,但这并不令人望而却步。
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