Realistic face animation generation from videos
원문 초록
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.
함께 살펴볼 논문
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Weakly-Supervised Multi-Face 3D Reconstruction
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
Self-supervised Learning of Detailed 3D Face Reconstruction
Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
토론
이 논문과 연결된 포럼 글이 아직 없습니다.