Watch faces morph into each other with StyleGAN 2, StyleGAN, and DCGAN!

Overview

FaceMorpher

FaceMorpher is an innovative project to get a unique face morph (or interpolation for geeks) on a website. Yes, this means you can see faces morphing into each other! These are created by the beautiful StyleGAN models, and DCGAN for comparison. Go ahead here.

Why Build It

Seems like a fun way to bring cool machine learning research to web development.

How it works

Machine Learning side

All these models are just GANs, which are basically just functions that take in a random vector and give an image (or in this case, a face.) So to see how the faces morph into each other, just see how the vector changes as you move it around the space. To see how two faces morph into each other, I choose two random vectors and see how as I move in the direction of the second vector from the first vector the faces generated change (make all the images a video.)

Web Development Side

FaceMorpher has a really screwed up unique software design.

All of the videos generated by the models are pre-generated (sorry, didn't want to keep you waiting for 2 minutes for a StyleGAN video on my CPU!) and hosted on an AWS S3 Bucket. When a video is requested from the frontend, an API I created (deployed on Heroku) will return the video link for a random video for it's "type" (stylegan(2), dcgan), to be used in the HTML to render the video. After the response, the API sends a request to my localtunnel to generate a new video (stylegan if a stylegan video url was returned, etc.) and upload that to the shared S3 Bucket. Talk about functional design!

Tech Stack

All platforms that I used:

  • Heroku
  • AWS S3 Bucket
  • Localtunnel
  • Svelte
  • Smelte
  • PyTorch

Acknowledgements

I did copy code to make the models and generate the videos. Stop pretending like you don't lmao

Owner
Anish
15-year old developer at Lynbrook High School with interest in machine learning and the theory that drives it.
Anish
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 08, 2023
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields

MINE: Continuous-Depth MPI with Neural Radiance Fields Project Page | Video PyTorch implementation for our ICCV 2021 paper. MINE: Towards Continuous D

Zijian Feng 325 Dec 29, 2022
TakeInfoatNistforICS - Take Information in NIST NVD for ICS

Take Information in NIST NVD for ICS This project developed with Python. When yo

5 Sep 05, 2022
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022
Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low L

Wenjing Wang 77 Dec 08, 2022
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation The code of: Context Decoupling Augmentation for Weakly Supervised Semanti

54 Dec 12, 2022
pcnaDeep integrates cutting-edge detection techniques with tracking and cell cycle resolving models.

pcnaDeep: a deep-learning based single-cell cycle profiler with PCNA signal Welcome! pcnaDeep integrates cutting-edge detection techniques with tracki

ChanLab 8 Oct 18, 2022
BARTScore: Evaluating Generated Text as Text Generation

This is the Repo for the paper: BARTScore: Evaluating Generated Text as Text Generation Updates 2021.06.28 Release online evaluation Demo 2021.06.25 R

NeuLab 196 Dec 17, 2022
A tensorflow implementation of an HMM layer

tensorflow_hmm Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms. See Keras example for an example of how to use

Zach Dwiel 283 Oct 19, 2022
Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships.

feature-set-comp Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships. Reposito

Trent Henderson 7 May 25, 2022
Pytorch Implementation of "Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation"

CRL_EGPG Pytorch Implementation of Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation We use contrastive loss implemented b

YHR 25 Nov 14, 2022
Finetuning Pipeline

KLUE Baseline Korean(한국어) KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark. See our paper fo

74 Dec 13, 2022
Learning Saliency Propagation for Semi-supervised Instance Segmentation

Learning Saliency Propagation for Semi-supervised Instance Segmentation PyTorch Implementation This repository contains: the PyTorch implementation of

Berkeley DeepDrive 68 Oct 18, 2022
Fast methods to work with hydro- and topography data in pure Python.

PyFlwDir Intro PyFlwDir contains a series of methods to work with gridded DEM and flow direction datasets, which are key to many workflows in many ear

Deltares 27 Dec 07, 2022
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

HUAWEI Noah's Ark Lab 3k Jan 08, 2023
Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

VIMuRe Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. If you use this code please cite this article (preprint). De

6 Dec 15, 2022
Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication"

NFFT4ANOVA Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication" This package uses th

Theresa Wagner 1 Aug 10, 2022
Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Static Features Classifier This is a static features classifier for Point-Could

ABDALKARIM MOHTASIB 1 Jan 25, 2022
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

Bilateral Denoising Diffusion Models (BDDMs) This is the official PyTorch implementation of the following paper: BDDM: BILATERAL DENOISING DIFFUSION M

172 Dec 23, 2022
Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN:Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022