Abstract
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.
Highlights
Blendshapes, a linear model for facial expressions, has become a standard approach for generating 3D facial models [1], with the blendshape models used in recent works [2]–[6] typically utilizing the facial action coding system (FACS) [7]
The global blendshape approach has the advantage of producing stable shapes while preventing the generation of unexpected expressions, it cannot correctly generate a face that has more than two expressions concurrently if the corresponding model is not included in the expression models
EXPERIMENTAL RESULTS We validated the performance of the proposed greedy pursuit approach for expression blendshapes by comparing its results with those from state-of-the-art methods that employ parametric facial models for face fitting
Summary
Blendshapes, a linear model for facial expressions, has become a standard approach for generating 3D facial models [1], with the blendshape models used in recent works [2]–[6] typically utilizing the facial action coding system (FACS) [7]. By utilizing a sparse set of delta blendshapes that are less redundant in relation to each other for fitting, expressive and reliable facial shapes can be generated without regularization.
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