Abstract: Motion blending is a widely used technique for character animation. The main idea is to blend similar motion examples according to blending weights, in order to synthesize new motions parameterizing high level characteristics of interest. We present in this paper an in-depth analysis and comparison of four motion blending techniques: Barycentric interpolation, Radial Basis Function, K-Nearest Neighbors and Inverse Blending optimization. Comparison metrics were designed to measure the performance across different motion categories on criteria including smoothness, parametric error and computation time. We have implemented each method in our character animation platform SmartBody and we present several visualization renderings that provide a window for gleaning insights into the underlying pros and cons of each method in an intuitive way.