Abstract: This paper surveys the overall methodologies for generating dynamic facial expression on 2D and 3D human face models and categorizes them into several classes based on their intrinsic properties. Even though there exists a considerable body of previous works, this topic is still gaining very active research attention. According to the dimensionality of their final output, the methods are categorized into class working on 2D image plane and class working on 3D facial manifold surfaces. The general approach of synthesizing 2D human facial expression includes: Feature-points driven facial expression synthesis method, generation methods based on facial proportion map, Laplacian transfer based expression generation method, methods based on general expression mapping functions, synthesis methods based on active expression model and the latest approaches using deep learning techniques. On the aspect of synthesizing 3D facial expression, the classifications contain: 3D shape regression-based methods, synthesis methods based on deformation, approaches based on pseudo muscle models, algorithms based on a motion-vector analysis, and the latest approaches using deep learning techniques. For each category, we describe its basic methodology along with its advantages and limitations. This survey paper is expected to help researchers to better position their potential future direction in the context of existing solutions.

Key words: facial expression generation, expression deforming transfer, facial expression exaggeration, facial aging effect