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EricGuo5513 / momask-codes

Official implementation of "MoMask: Generative Masked Modeling of 3D Human Motions (CVPR2024)"
https://ericguo5513.github.io/momask/
MIT License
690 stars 56 forks source link
3d-generation animation motion motion-generation motion-synthesis text-driven text-to-motion

readme

MoMask: Generative Masked Modeling of 3D Human Motions (CVPR 2024)

[Project Page] [Paper] [Huggingface Demo] [Colab Demo]

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If you find our code or paper helpful, please consider starring our repository and citing:

@article{guo2023momask,
      title={MoMask: Generative Masked Modeling of 3D Human Motions}, 
      author={Chuan Guo and Yuxuan Mu and Muhammad Gohar Javed and Sen Wang and Li Cheng},
      year={2023},
      eprint={2312.00063},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

:postbox: News

📢 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 for sharing the tutorial.

📢 2023-12-30 --- For easy WebUI BVH visulization, you could try this website bvh2vrma from this github.

📢 2023-12-29 --- Thanks to Camenduru for supporting the 🤗Colab demo.

📢 2023-12-27 --- Release WebUI demo. Try now on 🤗HuggingFace!

📢 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/1b3GnAbERH8jAoO5mdWgZhyxHB73n23sK?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/1lg62nugD7RTAIz0Q_YP2iZsxpUzzOkT1/view?usp=sharing) 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, Muse, vector-quantize-pytorch, T2M-GPT, MDM and MLD

License

This code is distributed under an MIT LICENSE.

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 cguo2@ualberta.ca for further questions.

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