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https://github.com/google-research/mint
Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.
https://github.com/google-research/mint
Last synced: 3 months ago
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Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.
- Host: GitHub
- URL: https://github.com/google-research/mint
- Owner: google-research
- License: apache-2.0
- Created: 2021-05-19T18:03:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-02T09:06:52.000Z (over 2 years ago)
- Last Synced: 2024-05-09T17:12:16.415Z (6 months ago)
- Language: Python
- Homepage:
- Size: 187 KB
- Stars: 486
- Watchers: 15
- Forks: 86
- Open Issues: 50
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ [ICCV-2021].
## Overview
This package contains the model implementation and training infrastructure of
our AI Choreographer.
## Get started
#### Pull the code
```
git clone https://github.com/liruilong940607/mint --recursive
```
Note here `--recursive` is important as it will automatically clone the submodule ([orbit](https://github.com/tensorflow/models/tree/master/orbit)) as well.
#### Install dependencies
```
conda create -n mint python=3.7
conda activate mint
conda install protobuf numpy
pip install tensorflow absl-py tensorflow-datasets librosa
sudo apt-get install libopenexr-dev
pip install --upgrade OpenEXR
pip install tensorflow-graphics tensorflow-graphics-gpu
git clone https://github.com/arogozhnikov/einops /tmp/einops
cd /tmp/einops/ && pip install . -U
git clone https://github.com/google/aistplusplus_api /tmp/aistplusplus_api
cd /tmp/aistplusplus_api && pip install -r requirements.txt && pip install . -U
```
Note if you meet environment conflicts about numpy, you can try with `pip install numpy==1.20`.
#### Get the data
See the [website](https://google.github.io/aistplusplus_dataset/)
#### Get the checkpoint
Download from google drive [here](https://drive.google.com/drive/folders/17GHwKRZbQfyC9-7oEpzCG8pp_rAI0cOm?usp=sharing), and put them to the folder `./checkpoints/`
#### Run the code
1. complie protocols
```
protoc ./mint/protos/*.proto
```
2. preprocess dataset into tfrecord
```
python tools/preprocessing.py \
--anno_dir="/mnt/data/aist_plusplus_final/" \
--audio_dir="/mnt/data/AIST/music/" \
--split=train
python tools/preprocessing.py \
--anno_dir="/mnt/data/aist_plusplus_final/" \
--audio_dir="/mnt/data/AIST/music/" \
--split=testval
```
3. run training
```
python trainer.py --config_path ./configs/fact_v5_deeper_t10_cm12.config --model_dir ./checkpoints
```
Note you might want to change the `batch_size` in the config file if you meet OUT-OF-MEMORY issue.
4. run testing and evaluation
```
# caching the generated motions (seed included) to `./outputs`
python evaluator.py --config_path ./configs/fact_v5_deeper_t10_cm12.config --model_dir ./checkpoints
# calculate FIDs
python tools/calculate_scores.py
```
## Citation
```bibtex
@inproceedings{li2021dance,
title={AI Choreographer: Music Conditioned 3D Dance Generation with AIST++},
author={Ruilong Li and Shan Yang and David A. Ross and Angjoo Kanazawa},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
year = {2021}
}
```