Home Projects HumanMAC
HumanMAC
Python

HumanMAC

[ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction".

by LinghaoChan · GitHub
Stars
Forks
License
Created
Last commit
Language
diffusion-modelsdiversitymotionMITPython
View on GitHub
In plain words

Run code to predict human motion based on previous movements, useful for applications in animation and robotics.

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 →
HumanMAC — GitHub preview card
📈 Star history
325324
2026-07-202026-08-31
📈 Track HumanMAC

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

[ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction".

HumanMAC has 324 stars on GitHub. It has been forked 20 times. HumanMAC is written mainly in Python. It has been in active development since 2023. HumanMAC is available under the MIT license. Its main topics are diffusion-models, diversity, motion, motion-forecasting.

Frequently asked questions

What is HumanMAC?

[ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction".

Is HumanMAC open source?

HumanMAC is an open-source project. It is released under the MIT license.

Is HumanMAC free?

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

What license does HumanMAC use?

HumanMAC is available under the MIT license.

What language is HumanMAC written in?

HumanMAC is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — HumanMAC

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

[![olud.ai](https://olud.ai/badge.php?tool=linghaochan-humanmac)](https://olud.ai/project/linghaochan-humanmac.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.