https://github.com/mingwuzheng/imface
공식 코드ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
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ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
A survey and classification of face alignment methods based on face models
Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
Learning a 3D Morphable Face Reflectance Model from Low-cost Data
High-Fidelity 3D Face Generation from Natural Language Descriptions
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.