This AI Selfie Transformation App Can Even Make Mona Lisa Smile

A new mobile app called FaceApp uses neural networks to edit your selfie via photo-realistic filters – letting you add a smile, swap genders, and can take years…

NVIDIA logo
NVIDIA
•
Brad Nemire
•1 min read•beginner•
--
•View Original
AzurePrismaTensorFlow

Overview

FaceApp is a mobile application that utilizes neural networks to transform selfies with photo-realistic filters, allowing users to add smiles, swap genders, and age themselves down. Developed in eight months by a small team in Russia, it leverages advanced technologies like CUDA and TensorFlow to achieve its effects.

What You'll Learn

1

How to use neural networks for photo editing in mobile applications

2

Why FaceApp differs from traditional style transfer applications

3

When to apply photo-realistic filters in user-generated content

Key Questions Answered

What technologies does FaceApp use for its transformations?
FaceApp utilizes CUDA, TITAN X Pascal GPUs, Tesla K80s, and the cuDNN-accelerated TensorFlow deep learning framework to train its convolutional neural networks. The app processes selfies in the cloud using Microsoft Azure’s Tesla K80 GPU instance.
How does FaceApp differ from Prisma in photo editing?
Unlike Prisma, which changes the style of a photo without altering its content, FaceApp modifies the content of the image, such as adding a smile, while maintaining the original photorealistic style. This distinction allows for more personalized transformations.
What is the development background of FaceApp?
FaceApp was developed and self-funded by a small group from Russia in just eight months. This rapid development highlights the potential for small teams to create impactful applications using advanced technology.

Technologies & Tools

Some links below are affiliate links. We may earn a commission if you make a purchase.

Backend
Cuda
Used for accelerating the training of deep learning models.
Hardware
Titan X Pascal Gpus
Utilized for processing and training neural networks.
Hardware
Tesla K80s
Employed in cloud computing for image transformation tasks.
Backend
Cudnn
Accelerates deep learning frameworks like TensorFlow.
Backend
Tensorflow
Framework used for training convolutional neural networks.
Cloud
Microsoft Azure
Provides cloud computing resources for processing selfies.

Key Actionable Insights

1
Consider leveraging neural networks for enhancing user engagement in mobile apps.
Using advanced photo editing features like those in FaceApp can significantly increase user interaction and retention by allowing users to personalize their content.
2
Explore cloud computing options for processing intensive image transformations.
By utilizing cloud services like Microsoft Azure, developers can offload heavy processing tasks, enabling smoother performance on mobile devices.
3
Differentiate your app by focusing on content modification rather than just style transfer.
Understanding the unique selling points of your application can help in marketing and attracting a specific user base looking for innovative features.