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Official implementation of "MoMask: Generative Masked Modeling of 3D Human Motions (CVPR2024)"
| Date | Stars |
|---|---|
| 2026-07-24 | 1302 |
| 2026-07-25 | 1302 |
| 2026-07-28 | 1302 |
| 2026-07-30 | 1302 |
| 2026-08-06 | 1302 |
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# MoMask: Generative Masked Modeling of 3D Human Motions (CVPR 2024)
### [[Project Page]](https://ericguo5513.github.io/momask) [[Paper]](https://arxiv.org/abs/2312.00063) [[Huggingface Demo]](https://huggingface.co/spaces/MeYourHint/MoMask) [[Colab Demo]](https://github.com/camenduru/MoMask-colab)
If you find our code or paper helpful, please consider starring our repository and citing:
```
@inproceedings{guo2024momask,
title={Momask: Generative masked modeling of 3d human motions},
author={Guo, Chuan and Mu, Yuxuan and Javed, Muhammad Gohar and Wang, Sen and Cheng, Li},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={1900--1910},
year={2024}
}
```
## :postbox: News
📢 **2024-08-02** --- The [WebUI demo 🤗](https://huggingface.co/spaces/MeYourHint/MoMask) is now running smoothly on a CPU. No GPU is required to use MoMask.
📢 **2024-02-26** --- 🔥🔥🔥 Congrats! MoMask is accepted to CVPR 2024.
📢 **2024-01-12** --- Now you can use MoMask in Blender as an add-on. Thanks to [@makeinufilm](https://twitter.com/makeinufilm) for sharing the [tutorial](https://medium.com/@makeinufilm/notes-on-how-to-set-up-the-momask-environment-and-how-to-use-blenderaddon-6563f1abdbfa).
📢 **2023-12-30** --- For easy WebUI BVH visulization, you could try this website [bvh2vrma](https://vrm-c.github.io/bvh2vrma/) from this [github](https://github.com/vrm-c/bvh2vrma?tab=readme-ov-file).
📢 **2023-12-29** --- Thanks to Camenduru for supporting the [🤗Colab](https://github.com/camenduru/MoMask-colab) demo.
📢 **2023-12-27** --- Release WebUI demo. Try now on [🤗HuggingFace](https://huggingface.co/spaces/MeYourHint/MoMask)!
📢 **2023-12-19** --- Release scripts for temporal inpainting.
📢 **2023-12-15** --- Release codes and models for momask. Including training/eval/generation scripts.
📢 **2023-11-29** --- Initialized the webpage and git project.
## :round_pushpin: Get You Ready
<details>
### 1. Conda Environment
```
conda env create -f environment.yml
conda activate momask
pip install git+https://github.com/openai/CLIP.git
```
We test our code on Python 3.7.13 and PyTorch 1.7.1
#### Alternative: Pip Installation
<details>
We provide an alternative pip installation in case you encounter difficulties setting up the conda environment.
```
pip install -r requirements.txt
```
We test this installation on Python 3.10
</details>
### 2. Models and Dependencies
#### Download Pre-trained Models
```
bash prepare/download_models.sh
```
#### Download Evaluation Models and Gloves
For evaluation only.
```
bash prepare/download_evaluator.sh
bash prepare/download_glove.sh
```
#### Troubleshooting
To address the download error related to gdown: "Cannot retrieve the public link of the file. You may need to change the permission to 'Anyone with the link', or have had many accesses". A potential solution is to run `pip install --upgrade --no-cache-dir gdown`, as suggested on https://github.com/wkentaro/gdown/issues/43. This should help resolve the issue.
#### (Optional) Download Manually
Visit [[Google Drive]](https://drive.google.com/drive/folders/1sHajltuE2xgHh91H9pFpMAYAkHaX9o57?usp=drive_link) to download the models and evaluators mannually.
### 3. Get Data
You have two options here:
* **Skip getting data**, if you just want to generate motions using *own* descriptions.
* **Get full data**, if you want to *re-train* and *evaluate* the model.
**(a). Full data (text + motion)**
**HumanML3D** - Follow the instruction in [HumanML3D](https://github.com/EricGuo5513/HumanML3D.git), then copy the result dataset to our repository:
```
cp -r ../HumanML3D/HumanML3D ./dataset/HumanML3D
```
**KIT**-Download from [HumanML3D](https://github.com/EricGuo5513/HumanML3D.git), then place result in `./dataset/KIT-ML`
####
</details>
## :rocket: Demo
<details>
### (a) Generate from a single prompt
```
python gen_t2m.py --gpu_id 1Excerpt of 12,030 characters
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matched fp:72d81a2a4b49db10, topic:animation, topic:motion-generation