Update README.md

README.md

@@ -1,258 +1,258 @@
----
-title: MomaskWorking
-app_file: app.py
-sdk: gradio
-sdk_version: 3.34.0
----
-# 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
-
-<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 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 `<text description>#<motion length>` 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 `<text description>#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/<ext>/`. 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.
-  
-</details>
-
-## :dancers: Visualization
-<details>
-
-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.
-
-
-</details>
-
-## :clapper: Temporal Inpainting
-<details>
-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`.
-
-</details>
-
-## :space_invader: Train Your Own Models
-<details>
-
-
-**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/<dataset_name>/<name>`
-* `--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/<dataset_name>/<name>`.
-</details>
-
-## :book: Evaluation
-<details>
-
-### 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/<dataset_name>/<name>/eval/<ext>.log`
-
-</details>
-
-## 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 cguo2@ualberta.ca 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)
+---
+title: MomaskWorking
+app_file: app.py
+sdk: gradio
+sdk_version: 6.0.1
+---
+# 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
+
+<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 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 `<text description>#<motion length>` 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 `<text description>#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/<ext>/`. 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.
+  
+</details>
+
+## :dancers: Visualization
+<details>
+
+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.
+
+
+</details>
+
+## :clapper: Temporal Inpainting
+<details>
+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`.
+
+</details>
+
+## :space_invader: Train Your Own Models
+<details>
+
+
+**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/<dataset_name>/<name>`
+* `--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/<dataset_name>/<name>`.
+</details>
+
+## :book: Evaluation
+<details>
+
+### 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/<dataset_name>/<name>/eval/<ext>.log`
+
+</details>
+
+## 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 cguo2@ualberta.ca 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)