biesseck Goto Github PK

3dfacepointcloudnet

Learning Directly from Synthetic Point Clouds for "In-the-wild" 3D Face Recognition

3dfacereconstfacerecogn

3D Face Reconstruction as Complementary Data to Enhance Face Recognition

app_calculadora

app_progiii_2bim

app_recados_ifmt_plc

benchmark-orientation

calculadora_ifmt

capface_android_app

Capface Android App - class attendance via facial recognition

capface_face_recognition_module

CapFace - face recognition module

capface_server

CapFace server backend

correct_plate_car_bbox

drone_dataset_license_plate

eg3d

EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks

face_liveness_detection

facex-zoo_biesseck

A PyTorch Toolbox for Face Recognition

image_triplet_loss

Image similarity using Triplet Loss

insightface

State-of-the-art 2D and 3D Face Analysis Project

insightface-tensorflow

liveness_dataset_tools

masktheface_biesseck

Convert face dataset to masked dataset

mesh

MPI-IS Mesh Processing Library

meta-pu

Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud (TVCG 2021)

mica

MICA - Towards Metrical Reconstruction of Human Faces [ECCV2022]

now_evaluation

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.

opensetfdic_ijcb2024_utils

pointnet2

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

pointnet2_pytorch

PyTorch implementation of Pointnet2/Pointnet++

projeto2bim

projeto_teste_github

python-pcl

Python bindings to the pointcloud library (pcl)