papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Extreme 3D Face Reconstruction: Looking Past Occlusions

Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild

A high-fidelity 3D face reconstruction library from monocular RGB image(s)

This is a implementation of the 3D FLAME model in PyTorch

A 3DMM fitting framework using Pytorch.

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)

[CVPR2023] A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images.

Tensorflow framework for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 2D or 3D keypoints, and how to generate textured head meshes from Images.

Facial Landmark Detection and head pose compute use dlib, Real time Face Reconstruction use 3D Morphable Face Model fitting

[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.

Official Pytorch Implementation of SPECTRE: Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos

A collection of face related papers

Demo and Database of "DeepSketch2Face" (SIGGRAPH 2017)

Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation (ECCV2022)

[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

3D human face estimation and rendering from a single image

This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

有空就写点，没空就空着。