Home Projects TP-GAN
TP-GAN
Python

TP-GAN

Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"

by HRLTY · GitHub
Stars
Forks
Created
Last commit
Language
Likely
Self-hostable
computer-visionface-recognitiongenerative-adversarial-networkPythonSelf-hostable
View on GitHub
In plain words

Generate realistic frontal face images from side views using a specific AI model.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
TP-GAN — GitHub preview card
📈 Star history
511510
2026-07-202026-08-31
📈 Track TP-GAN

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"

TP-GAN has 510 stars on GitHub. It has been forked 127 times. TP-GAN is written mainly in Python. It has been in active development since 2018. Its main topics are computer-vision, face-recognition, generative-adversarial-network, synthesis.

Frequently asked questions

What is TP-GAN?

Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"

Is TP-GAN open source?

TP-GAN is an open-source project.

Is TP-GAN free?

Yes. TP-GAN is free and open source — you can use, modify and self-host it.

What language is TP-GAN written in?

TP-GAN is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — TP-GAN

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=hrlty-tp-gan)](https://olud.ai/project/hrlty-tp-gan.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.