Stabilized Temporal 3D Face Alignment Using Landmark Displacement Learning

Abstract

One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome the problem, we introduce neural networks to reconstruct stable and precise faces in time. The reconstruction network extracts the 3DMM parameters from video sequences to represent 3D faces in time. Meanwhile, our displacement networks learn the changes in facial landmarks. In particular, the networks learn changes caused by facial identity, facial expression, and temporal cues, respectively. The proposed facial alignment network exhibits reliable and precise performance in reconstructing static and dynamic faces by leveraging these displacement networks. The 300 Videos in the Wild (300VW) dataset is utilized for qualitative and quantitative evaluations to confirm the effectiveness of our method. The results demonstrate the considerable advantages of our method in reconstructing 3D faces from video sequences.