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aideveloper-oz's Projects

graphcmr icon graphcmr

Repository for the paper "Convolutional Mesh Regression for Single-Image Human Shape Reconstruction"

hairnets icon hairnets

Hair Segmentation and Classification with Unet and GoogleNet

hairstyle icon hairstyle

U-net model to automatically segment the hair with the goal to build a system that shows people how they will look with different hairstyles.

handai icon handai

Using hand gestures control different effect of photography.

hidt icon hidt

Official repository for the paper High-Resolution Daytime Translation Without Domain Labels (CVPR2020, Oral)

hifi3dface icon hifi3dface

Code and data for our paper "High-Fidelity 3D Digital Human Creation from RGB-D Selfies".

hub icon hub

A library for transfer learning by reusing parts of TensorFlow models.

ict-facekit icon ict-facekit

ICT's Vision and Graphics Lab's morphable face model and toolkit

linear-regression_r icon linear-regression_r

The objective of this study is to determine whether a human body circumference measurement could be used as a general indicator for human body fat percentage. A such body circumference measurement could then be used to predict the body fat percentage by establishing a simple linear formula. The study will further assess how well this linear formula perform to estimate the body fat percentage by comparing the predicted values against the real values. All the statistical computations have been performed in ‘R Studio’ package in this study. A data-set of 252 people (160 male and 92 female) with their body fat percentages (Brozek method) and ten different body circumference measurements have been used in this study. The Source for the data-set: Roger W. Johnson. March 1996. Fitting Percentage of Body Fat to Simple Body Measurements. Journal of Statistics Education, Volume 4, Number 1.

lstm-human-activity-recognition icon lstm-human-activity-recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier

mlquestions icon mlquestions

Machine Learning and Computer Vision Engineer - Technical Interview Questions

models icon models

Pre-trained and Reproduced Deep Learning Models (『飞桨』官方模型库,包含多种学术前沿和工业场景验证的深度学习模型)

mvfnet icon mvfnet

Pytorch code for paper: MVF-Net: Multi-View 3D Face Morphable Model Regression

nailtracking icon nailtracking

Real-time Nail-Detection using Neural Networks (SSD) on Tensorflow.

navigatar icon navigatar

Finding your way through buildings and campuses through the power of augmented reality

opencv icon opencv

Open Source Computer Vision Library

pa-gan-tensorflow icon pa-gan-tensorflow

PA-GAN Tensorflow, PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing

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