Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions
提出广义拉普拉斯特征映射降维方法处理时间序列的风格变化，并设计基于图的粒子滤波器在低维空间高效跟踪人体姿态，实验表明在欠约束场景下达到最优性能。
This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.