A review of motion retargeting techniques for 3D character facial animation

Abstract

3D face animation has been a critical component of character animation in a wide range of media since the early 90’s. The conventional process for animating a 3D face is usually keyframe-based, which is labor-intensive. Therefore, the film and game industries have started using live-action actors’ performances to animate the faces of 3D characters, the process is also known as performance-driven facial animation. At the core of performance-driven facial animation is facial motion retargeting, which transfers the source facial motions to a target 3D face. However, facial motion retargeting still has many limitations that influence its capability to further assist the facial animation process. Existing motion retargeting frameworks cannot accurately transfer the source motion’s semantic information (i.e., meaning and intensity of the motion), especially when applying the motion to non-human-like or stylized target characters. The retargeting quality relies on the parameterization of the target face, which is time-consuming to build and usually not generalizable across proportionally different faces. In this survey paper, we review the literature relating to 3D facial motion retargeting methods and the relevant topics within this area. We provide a systematic understanding of the essential modules of the retargeting pipeline, a taxonomy of the available approaches under these modules, and a thorough analysis of their advantages and limitations with research directions that could potentially contribute to this area. We also contributed a 3D character categorization matrix, which has been used in this survey and might be useful for future research to evaluate the character compatibility of their retargeting or face parameterization methods.