MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations (SIGGRAPH 2024 Journal Track)

Project Page: https://moconvq.github.io/

The code provides:

• Pretrained model and code for MoConVQ representation
• Pretrained model and code for MoConGPT
• Trainging code for MoConVQ
• Trainging code for MoConGPT

Install

Environment installation is a bit complicated, so we have prepared a script for installation, please refer to setup.cmd

Pretrained data: Download from

https://disk.pku.edu.cn/link/AAAFE3B2DDB1AC420EB5C4E0910196116F

and place all file in this folder

Motion Reconstruction

The moconvq_base.data contains a motion encoder and a physiscs-based motion decoder.

Please refer to .\Script\track_something.py to get more information about the output of the motion encoder.

Or you can run the following command to reconstruct a kinematic motion into physics-based version

python .\Script\track_something.py base.bvh

Unconditional motion generation

python .\Script\unconditional_generation.py

text-to-motion generation

First install some additional packages:

pip install transformers sentencepiece

Then run the code:

 python .\Script\text2motion_generation.py

text description can be found in the python script