论文标题
通过因素分析仪的约束混合物在形状种群内的学习构成变化
Learning pose variations within shape population by constrained mixtures of factor analyzers
论文作者
论文摘要
采矿和学习基础种群的形状变异性使应用程序受益，包括参数形状建模，3D动画和图像分割。当前的统计形状建模方法在学习非结构化形状变化的情况下非常有效，而没有明显的姿势变化（身体部位的相对旋转）。研究形状种群中的姿势变化涉及将形状分割为不同的铰接部分，并学习分段部分的转换。本文将姿势学习问题提出为因素分析仪的混合物。分割是通过组件后概率获得的，姿势变化中的旋转是通过因子加载矩阵来学习的。为了确保因子加载矩阵由旋转矩阵组成，施加了约束，并得出了相应的封闭形式的最佳解决方案。基于提出的方法，从给定的形状种群中自动学习姿势变化。该方法是在运动动画中应用的，在该动画中，通过插值训练集中的现有姿势来生成新的姿势。获得的结果是平稳和现实的。
Mining and learning the shape variability of underlying population has benefited the applications including parametric shape modeling, 3D animation, and image segmentation. The current statistical shape modeling method works well on learning unstructured shape variations without obvious pose changes (relative rotations of the body parts). Studying the pose variations within a shape population involves segmenting the shapes into different articulated parts and learning the transformations of the segmented parts. This paper formulates the pose learning problem as mixtures of factor analyzers. The segmentation is obtained by components posterior probabilities and the rotations in pose variations are learned by the factor loading matrices. To guarantee that the factor loading matrices are composed by rotation matrices, constraints are imposed and the corresponding closed form optimal solution is derived. Based on the proposed method, the pose variations are automatically learned from the given shape populations. The method is applied in motion animation where new poses are generated by interpolating the existing poses in the training set. The obtained results are smooth and realistic.
