Flickr-Faces-HQ Dataset (FFHQ)

The official repository of our CVPR2023 paper "FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction".

MaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ).

A dataset that contains 6,000 non-hair FFHQ portraits.

FFHQ-Aging Dataset

FFHQR -- the first large-scale retouching dataset for computer vision research.

Face alignment tool for transforming face images into FFHQ-style.

[FG 2021🎈] A small-scale face image dataset with large-scale facial attributes for text-to-face generation and manipulation.

FFHQ-UV-Intrinstics: A Dataset Containing Intrinsic Face Decomposition

[CVPR '23] Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models

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Train a U-net model for pixel-wise segmentation of facial wrinkles. Based on FFHQ dataset.

PointFaces: A dataset created from the FFHQ Dataset. Includes detected point cloud facial landmark captures done with MediaPipe (.PLY) and (.PNG). Comes with BlendShape facial 3D models based on the canonical face from MediaPipe.

"FFHQ Ageing" is a dataset of human faces designed by NVidia for benchmarking age transformation algorithms as well as many other possible vision tasks.

FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles

Implemented VQGAN + Transformer for diverse image generation using FFHQ dataset.

패치 기반 딥페이크 영상 검출에 관한 연구

Pro-UGATIT is an advanced unsupervised image-to-image translation model, building upon UGATIT with progressive learning and Adaptive Layer Instance Normalization (AdaLIN). It supports high-quality translation between real and anime-style faces using datasets like FFHQ and Danbooru2021.

Extracts and aligns all faces from images using DLib and a function from original FFHQ dataset preparation step

Built a deep learning model which takes any image of face and outputs an image with acne and pigmentation eliminated, trained with Flickr-Faces-HQ Dataset (FFHQ) dataset as source and FFHQR dataset as target.

A learning project wherein I implemented a CNN based autoencoder for denoising images. Used the PyTorch framework.Trained separate models on BSD100 and FFHQ datasets with gaussian noise.

This work was done on the basis of a PyTorch [tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html). [Flickr Faces](https://github.com/NVlabs/ffhq-dataset)

This repository contains my final submission for the COMP3547 Deep Learning module assignment at Durham University in the academic year 2022/2023. The project focuses on diffusion-based models and their application in synthesising new, unique images, which could plausibly come from a training data set. Final grade received was 71/100.

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Projected GAN on ffhq dataset

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StyleGAN2-ADA model on FFHQ dataset