Flexible editing of style, identity and content of human motion

Many prior works aim to provide style editing approaches which are intuitive and straightforward, such as [Min et al. 2010] etc. But they did not consider the content editing, like generate a running motion for an actor given his walking motion. For the purpose of style editing, most researches use linear model, such as PCA, ICA and multilinear model [Min et al. 2010]. However, the human motion is actually a highly nonlinear model [Elgammal and Lee 2004]. But the nonlinear model can not construct a direct mapping which makes the reconstruction of high-dimensional data complex. Besides, it can only manipulate data in the training database. In addition, when extracting the style of motion, most methods do not separate the content before learning the style. This confuses the content and style when doing the style learning, and therefore impairs the final result.