Realistic face animation generation from videos
This is an incremental improvement for computer vision applications like face animation.
The paper tackles the problem of 3D face reconstruction and face alignment by analyzing three state-of-the-art methods and proposing improvements to PRN to enhance accuracy and speed, though no concrete numbers are provided.
3D face reconstruction and face alignment are two fundamental and highly related topics in computer vision. Recently, some works start to use deep learning models to estimate the 3DMM coefficients to reconstruct 3D face geometry. However, the performance is restricted due to the limitation of the pre-defined face templates. To address this problem, some end-to-end methods, which can completely bypass the calculation of 3DMM coefficients, are proposed and attract much attention. In this report, we introduce and analyse three state-of-the-art methods in 3D face reconstruction and face alignment. Some potential improvement on PRN are proposed to further enhance its accuracy and speed.