Realistic face animation generation from videos
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 27 Mar 2021.
Builds on
8 of 29 references.
References · 8
- 2020Towards Fast, Accurate and Stable 3D Dense Face Alignmentcited by 219
- 2018Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Networkcited by 195
- 2016Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Networkcited by 109
- 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
- 2017Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regressioncited by 66
- 2017Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognitioncited by 21
- 2021Subjective and Objective Visual Quality Assessment of Textured 3D Meshescited by 13
- 2015Fitting 3D Morphable Models using Local Featurescited by 6
Built on by
Citing papers per year: 2021: 1.
1 paper.