A Greedy Pursuit Approach for Fitting 3D Facial Expression Models

We propose a novel fitting strategy for the expression of blendshapes. Rather than employing all of the expression blendshapes to approximate the target points, only a subset of blendshapes selected to represent an expression on the target face is utilized, which efficiently reduces redundancy among the expression models. An expression correlation map is proposed to measure the redundancies between the blendshapes under the assumption that each expression changes the facial shape regionally, which enables a few less-correlated expressions to be obtained using a greedy pursuit approach. It is demonstrated that a subset of blendshapes that represents the target more expressively and semantically can be obtained non-parametrically using the proposed selection method, which enables natural facial shapes to be reliably generated without regularization, while also coping well with target-specific or unusual expressions. The experimental results from public datasets exhibit an increase in the quality of the facial shapes and expressions over baseline methods and state-of-the-art facial fitting approaches.