Facial expression synthesis from a single image
Man-Chia Chang, Ming‐Sui Lee · 2014
Facial expression synthesis has drawn a lot of attention in many applications, such as facial animation and human-computer interactions. Some expression synthesis methods are conducted in 2D domain where only images are taken as input but the muscle deformation is usually neglected regardless of different expressions. Methods performed in 3D domain generate more natural synthesized images but require a 3D model of the input and suffer high computational complexity, which makes it inapplicable to certain situations. A facial expression synthesis method which combines the advantages of 2D and 3D methods is proposed in this paper to synthesize expressions on an input neutral facial image. More accurate geometry information is exploited from 3D models by applying a time-saving face model reconstruction method. Expression on 2D is then synthesized using the information from 3D to produce a natural synthesized facial image with desired expression. To obtain the expressive image, the displacements of 48 facial feature points are utilized to approximate all the displacement for the whole face. Experimental results demonstrate that the proposed system can generate facial images of various expressions with satisfactory quality.