code used on the paper Face Reconstruction with Variational Autoencoder and Face Masks https://arxiv.org/abs/2112.02139
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Updated
Jul 23, 2024 - Python
code used on the paper Face Reconstruction with Variational Autoencoder and Face Masks https://arxiv.org/abs/2112.02139
[CVPR2023] A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images.
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)
[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.
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
Geometry-aware Face Reconstruction
A 3DMM fitting framework using Pytorch.
[ICCVW 2023] TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces. 1st place at VSCHH @ ICCV 2023.
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation (ECCV2022)
Face Reconstructor: Mediapipe Holistic + OpenCV for accurate facial landmark detection and mesh-based skin patching
This is a implementation of the 3D FLAME model in PyTorch
Official Pytorch Implementation of SPECTRE: Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
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.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
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.
Facial Depth and Normal Estimation using Dual-Pixel Camera (ECCV 22)
Reconstruct depth face image
Python project on “Finding set of faces when combined results in face of person A". The application is deployed as PIP Library.
[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
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