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https://github.com/ericguo5513/momask-codes

Official implementation of "MoMask: Generative Masked Modeling of 3D Human Motions (CVPR2024)"
https://github.com/ericguo5513/momask-codes

3d-generation animation motion motion-generation motion-synthesis text-driven text-to-motion

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Official implementation of "MoMask: Generative Masked Modeling of 3D Human Motions (CVPR2024)"

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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)

![teaser_image](https://ericguo5513.github.io/momask/static/images/teaser.png)



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



  

### 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



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



### 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`



#### 



## :rocket: Demo



### (a) Generate from a single prompt

```

python gen_t2m.py --gpu_id 1 --ext exp1 --text_prompt "A person is running on a treadmill."

```

### (b) Generate from a prompt file

An example of prompt file is given in `./assets/text_prompt.txt`. Please follow the format of `#` at each line. Motion length indicates the number of poses, which must be integeter and will be rounded by 4. In our work, motion is in 20 fps.



If you write `#NA`, our model will determine a length. Note once there is **one** NA, all the others will be **NA** automatically.



```

python gen_t2m.py --gpu_id 1 --ext exp2 --text_path ./assets/text_prompt.txt

```



A few more parameters you may be interested:

* `--repeat_times`: number of replications for generation, default `1`.

* `--motion_length`: specify the number of poses for generation, only applicable in (a).



The output files are stored under folder `./generation//`. They are

* `numpy files`: generated motions with shape of (nframe, 22, 3), under subfolder `./joints`.

* `video files`: stick figure animation in mp4 format, under subfolder `./animation`.

* `bvh files`: bvh files of the generated motion, under subfolder `./animation`.



We also apply naive foot ik to the generated motions, see files with suffix `_ik`. It sometimes works well, but sometimes will fail.

  



## :dancers: Visualization



All the animations are manually rendered in blender. We use the characters from [mixamo](https://www.mixamo.com/#/). You need to download the characters in T-Pose with skeleton.



### Retargeting

For retargeting, we found rokoko usually leads to large error on foot. On the other hand, [keemap.rig.transfer](https://github.com/nkeeline/Keemap-Blender-Rig-ReTargeting-Addon/releases) shows more precise retargetting. You could watch the [tutorial](https://www.youtube.com/watch?v=EG-VCMkVpxg) here.



Following these steps:

* Download keemap.rig.transfer from the github, and install it in blender.

* Import both the motion files (.bvh) and character files (.fbx) in blender.

* `Shift + Select` the both source and target skeleton. (Do not need to be Rest Position)

* Switch to `Pose Mode`, then unfold the `KeeMapRig` tool at the top-right corner of the view window.

* For `bone mapping file`, direct to `./assets/mapping.json`(or `mapping6.json` if it doesn't work), and click `Read In Bone Mapping File`. This file is manually made by us. It works for most characters in mixamo.

* (Optional) You could manually fill in the bone mapping and adjust the rotations by your own, for your own character. `Save Bone Mapping File` can save the mapping configuration in local file, as specified by the mapping file path.

* Adjust the `Number of Samples`, `Source Rig`, `Destination Rig Name`.

* Clik `Transfer Animation from Source Destination`, wait a few seconds.



We didn't tried other retargetting tools. Welcome to comment if you find others are more useful.



### Scene



We use this [scene](https://drive.google.com/file/d/16SbrnG9JsJ2w7UwCFmh10PcBdl6HxlrA/view?usp=drive_link) for animation.



## :clapper: Temporal Inpainting



We conduct mask-based editing in the m-transformer stage, followed by the regeneration of residual tokens for the entire sequence. To load your own motion, provide the path through `--source_motion`. Utilize `-msec` to specify the mask section, supporting either ratio or frame index. For instance, `-msec 0.3,0.6` with `max_motion_length=196` is equivalent to `-msec 59,118`, indicating the editing of the frame section [59, 118]. 



```

python edit_t2m.py --gpu_id 1 --ext exp3 --use_res_model -msec 0.4,0.7 --text_prompt "A man picks something from the ground using his right hand."

```



Note: Presently, the source motion must adhere to the format of a HumanML3D dim-263 feature vector. An example motion vector data from the HumanML3D test set is available in `example_data/000612.npy`. To process your own motion data, you can utilize the `process_file` function from `utils/motion_process.py`.



## :space_invader: Train Your Own Models



**Note**: You have to train RVQ **BEFORE** training masked/residual transformers. The latter two can be trained simultaneously.



### Train RVQ

You may also need to download evaluation models to run the scripts.

```

python train_vq.py --name rvq_name --gpu_id 1 --dataset_name t2m --batch_size 256 --num_quantizers 6  --max_epoch 50 --quantize_dropout_prob 0.2 --gamma 0.05

```



### Train Masked Transformer

```

python train_t2m_transformer.py --name mtrans_name --gpu_id 2 --dataset_name t2m --batch_size 64 --vq_name rvq_name

```



### Train Residual Transformer

```

python train_res_transformer.py --name rtrans_name  --gpu_id 2 --dataset_name t2m --batch_size 64 --vq_name rvq_name --cond_drop_prob 0.2 --share_weight

```



* `--dataset_name`: motion dataset, `t2m` for HumanML3D and `kit` for KIT-ML.  

* `--name`: name your model. This will create to model space as `./checkpoints//`

* `--gpu_id`: GPU id.

* `--batch_size`: we use `512` for rvq training. For masked/residual transformer, we use `64` on HumanML3D and `16` for KIT-ML.

* `--num_quantizers`: number of quantization layers, `6` is used in our case.

* `--quantize_drop_prob`: quantization dropout ratio, `0.2` is used.

* `--vq_name`: when training masked/residual transformer, you need to specify the name of rvq model for tokenization.

* `--cond_drop_prob`: condition drop ratio, for classifier-free guidance. `0.2` is used.

* `--share_weight`: whether to share the projection/embedding weights in residual transformer.



All the pre-trained models and intermediate results will be saved in space `./checkpoints//`.



## :book: Evaluation



### Evaluate RVQ Reconstruction:

HumanML3D:

```

python eval_t2m_vq.py --gpu_id 0 --name rvq_nq6_dc512_nc512_noshare_qdp0.2 --dataset_name t2m --ext rvq_nq6



```

KIT-ML:

```

python eval_t2m_vq.py --gpu_id 0 --name rvq_nq6_dc512_nc512_noshare_qdp0.2_k --dataset_name kit --ext rvq_nq6

```



### Evaluate Text2motion Generation:

HumanML3D:

```

python eval_t2m_trans_res.py --res_name tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw --dataset_name t2m --name t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6ns --gpu_id 1 --cond_scale 4 --time_steps 10 --ext evaluation

```

KIT-ML:

```

python eval_t2m_trans_res.py --res_name tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw_k --dataset_name kit --name t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6ns_k --gpu_id 0 --cond_scale 2 --time_steps 10 --ext evaluation

```



* `--res_name`: model name of `residual transformer`.  

* `--name`: model name of `masked transformer`.  

* `--cond_scale`: scale of classifer-free guidance.

* `--time_steps`: number of iterations for inference.

* `--ext`: filename for saving evaluation results.

* `--which_epoch`: checkpoint name of `masked transformer`.



The final evaluation results will be saved in `./checkpoints///eval/.log`



## Acknowlegements



We sincerely thank the open-sourcing of these works where our code is based on: 



[deep-motion-editing](https://github.com/DeepMotionEditing/deep-motion-editing), [Muse](https://github.com/lucidrains/muse-maskgit-pytorch), [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantize-pytorch), [T2M-GPT](https://github.com/Mael-zys/T2M-GPT), [MDM](https://github.com/GuyTevet/motion-diffusion-model/tree/main) and [MLD](https://github.com/ChenFengYe/motion-latent-diffusion/tree/main)



## License

This code is distributed under an [MIT LICENSE](https://github.com/EricGuo5513/momask-codes/tree/main?tab=MIT-1-ov-file#readme).



Note that our code depends on other libraries, including SMPL, SMPL-X, PyTorch3D, and uses datasets which each have their own respective licenses that must also be followed.



### Misc

Contact [email protected] for further questions.



## Star History



[![Star History Chart](https://api.star-history.com/svg?repos=EricGuo5513/momask-codes&type=Date)](https://star-history.com/#EricGuo5513/momask-codes&Date)