Introduction

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XRMoGen is a codebase for motion generation. Currently, it contains two dance generation algorithms

• Bailando: Siyao et al., Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory, CVPR 2022
• DanceRevolution: Huang et al., Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning, ICLR 2021

For environment settings, see installation.md.

• Getting Started
  • Datasets
  • Build a Model
  • Write a new dance generation model
  • Train a Model
  • Iteration Controls
  • Train
  • Test
  • Visualize
  • Tutorials

Datasets

We recommend using pre-extracted features for music and motion data, see dataset_preparation.md. After downloading, extract it to $PROJECT/data. In order to facilitate the synthesis of a video with music after the dance is generated, it is needed to download the original music (.mov) to the musics folder in the same directory:

xrmogen ├── mogen ├── docs ├── configs ├── data │ ├── aistpp_train_wav │ ├── aistpp_test_full_wav │ ├── aistpp_music_feat_7.5fps │ ├── aist_features_zero_start │ ├── musics ├── ...

Build a Model

Write a new dance generation model

The model structure can be customized through config files. To implement a new method, your model need to contain following functions/medhotds to fit the training/test pipeline:

• train_step(): forward method of the training mode.
• val_step(): forward method of the testing mode.
• registered as a dance model

To be specific, if we want to implement a new model, there are several things to do.

1. create a new file in mogen/models/dance_models/my_model.py.

   ```python from ..builder import NETWORKS from ...builder import DANCE_MODELS

   @DANCEMODELS.registermodule() class MyDanceModel(nn.Module):

   def __init__(self, model_config):
       super().__init__()
   
   def forward(self, ...):
       ....
   
   def train_step(self, data, optimizer, **kwargs):
       ....
   
   def val_step(self, data, optimizer=None, **kwargs):
       ....
   

   ```

2. import the model in mogen/models/__init__.py

   python from .my_model import MyDanceModel

3. write a config file that defines the model as

   python model = dict( type='MyDanceModel', ....

Train a Model

Epoch Controls

XRMoGen uses mmcv.runner.EpochBasedRunner to control training and test.

In the training mode, the max_epochs in config file decide how many epochs to train. In test mode, max_epochs is forced to change to 1, which represents only 1 epoch to test.

Validation frequency is set as workflow of config file: python workflow = [('train', 20), ('val', 1)]

Train

For example, to train Bailando (Dance Revolution),

shell python main.py --config configs/dance_rev.py

Arguments are: - --config: config file path.

Test

To test relevant model, add --test_only tag after the config path. We provide some pretrained weights to test (see pretrainedmodellist.md. Download the pretrained weights under a folder ./example, and run

shell python main.py --config configs/bailando_test.py --test_only to generate the dance poses. The poses will be stored under the workdir ("./bailando_test" in this case) set in config file.

To Compute the quantitative scores: ```python python tools/evalquantitativescores.py --pklroot ./bailandotest/test/epoch0 --gtroot data/aistfeatureszerostart --musicfeatureroot data/aistpptestfull_wav

```

The results should be aligned with benchmark.md.

Visualize

python python tools/visualize_dance_from_pkl.py --pkl_root ./bailando_test/test/epoch0 --audio_path data/musics/

Tutorials

Currently, we provide some tutorials for users to learn about * configs * data pipeline * model

Other Documents

Except for that，The document also includes the following * api * dataset_preparation * installation

License

The license of our codebase is Apache-2.0. Note that this license only applies to code in our library, the dependencies of which are separate and individually licensed. We would like to pay tribute to open-source implementations to which we rely on. Please be aware that using the content of dependencies may affect the license of our codebase. Refer to LICENSE to view the full license.

Citation

If you find this project useful in your research, please consider cite:

bibtex @misc{xrmogen, title={OpenXRLab Motion Generation Codebase}, author={XRMoGen Contributors}, howpublished = {\url{https://github.com/openxrlab/xrmogen}}, year={2022} }

Contributing

We appreciate all contributions to improve XRMoGen. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

XRMoGen is an open source project that is contributed by researchers and engineers from both the academia and the industry. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the framework and benchmark could serve the growing research community by providing a flexible framework to reimplement existing methods and develop their own new models.

Projects in OpenXRLab

• XRPrimer: OpenXRLab foundational library for XR-related algorithms.
• XRSLAM: OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.
• XRSfM: OpenXRLab Structure-from-Motion Toolbox and Benchmark.
• XRLocalization: OpenXRLab Visual Localization Toolbox and Server.
• XRMoCap: OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.
• XRMoGen: OpenXRLab Human Motion Generation Toolbox and Benchmark.
• XRNeRF: OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.