This repository contains the implementation of the paper "MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls"
Keywords: Whole-body Motion Generation, Multimodal Controls, Text-to-Motion, Music-to-Dance, Speech-to-Gesture
Yuxuan Bian1, Ailing Zeng2*, Xuan Ju1, Xian Liu1, Zhaoyang Zhang1, Wei Liu2, Qiang Xu1*
1The Chinese University of Hong Kong 2Tencent *Corresponding Author
🌐Project Page | 📜Arxiv | 📃PDF | 🗄️Data | 📹Video
📖 Table of Contents
- Release evluation and visualization code
- Release MC-Bench pre-processing code
- Release checkpoints
- Release trainig code
- Release gradio demo
We present MotionCraft, a diffusion transformer that crafts whole-body motion with plug-and-play multimodal controls, encompassing robust motion generation abilities including Text-to-Motion, Speech-to-Gesture, and Music-to-Dance.
Whole-body multimodal motion generation, controlled by text, speech, or music, has numerous applications including video generation and character animation. However, employing a unified model to achieve various generation tasks with different condition modalities presents two main challenges: motion distribution drifts across different tasks (e.g., co-speech gestures and text-driven daily actions) and the complex optimization of mixed conditions with varying granularities (e.g., text and audio). Additionally, inconsistent motion formats across different tasks and datasets hinder effective training toward multimodal motion generation. In this paper, we propose MotionCraft, a unified diffusion transformer that crafts whole-body motion with plug-and-play multimodal control. Our framework employs a coarse-to-fine training strategy, starting with the first stage of text-to-motion semantic pre-training, followed by the second stage of multimodal low-level control adaptation to handle conditions of varying granularities. To effectively learn and transfer motion knowledge across different distributions, we design MC-Attn for parallel modeling of static and dynamic human topology graphs. To overcome the motion format inconsistency of existing benchmarks, we introduce MC-Bench, the first available multimodal whole-body motion generation benchmark based on the unified SMPL-X format. Extensive experiments show that MotionCraft achieves state-of-the-art performance on various standard motion generation tasks.
MotionCraft has been implemented and tested on Pytorch 1.12.1 with python 3.9.
Clone the repo:
git clone https://github.com/cure-lab/MotionCraft.git
We recommend you first use conda to create virtual environment, and install pytorch following official instructions. For example:
conda create -n motioncraft python=3.9 -y
conda activate motioncraft
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch -y
Then, you can install mmcv with:
pip install "mmcv-full>=1.4.2,<=1.9.0"
After that, you can install required packages thourgh:
cd MotionCraft
pip install -r requirements.txt
Dataset
You can download all the original motion datasets and process them into the MC-Bench as follows, which are used for training and testing the BrushNet. By downloading the data, you are agreeing to the terms and conditions of the license. The data structure should be like:
|-- data
|-- datasets
|-- beats2/PantoMatrix
|-- BEAT2
|-- beat_english_v2.0.0
|-- ...
|-- datasets/beat_cache
|-- beat_smplx_en_emage
|-- beat_smplx_en_emage_test
|-- EMAGE
|-- mean.npy
|-- std.npy
|-- ...
|-- finedance
|-- label_json
|-- motion
|-- motion_fea163
|-- music_npy
|-- music_wav
|-- mean.npy
|-- std.npy
|-- ...
|-- motionx
|-- motion_data/smplx_322/humanml
|-- 000002.npy
|-- 000003.npy
|-- ...
|-- texts/semantic_labels/humanml
|-- 000002.txt
|-- 000003.txt
|-- humanml3d_clip_align.py
|-- humanml3d_align_mean.npy
|-- humanml3d_align_std.npy
|-- humanml3d_align_test.txt
|-- humanml3d_align_train_val.txt
|-- humanml3d_origin_test.txt
|-- humanml3d_origin_train_val.txt
|-- evaluators
|-- human_ml3d
|-- finest.tar
|-- smplx322
|-- distilbert-base-uncased
|-- epoch=199.ckpt
Noted: Due to dataset license limitation, we can not redistribute the processed MC-Bench here. However, we provide detailed steps to construct from the original data sources.
Text-to-Motion Benchmark Construction
-
Following the processing workflow of Mocap Datasets in MotionX, obtain a processed subset of SMPL-X HumanML3D from the raw AMASS dataset.
-
Follow the official preparation to align each motion data with different text annotations and different time intervals by running the following code:
cd ./data/datasets/motionx mkdir motionx/motion_data/smplx_322/humanml3d_align_test mkdir motionx/motion_data/smplx_322/humanml3d_align_train_val mkdir motionx/texts/semantic_labels/humanml3d_align_test mkdir motionx/texts/semantic_labels/humanml3d_align_train_val python humanml3d_clip_align.py rm -rf motionx/motion_data/smplx_322/humanml rm -rf motionx/texts/semantic_labels/humanml mv motionx/motion_data/smplx_322/humanml3d_align_test/* motionx/motion_data/smplx_322/humanml3d_align_train_val/ mv motionx/texts/semantic_labels/humanml3d_align_test/* motionx/texts/semantic_labels/humanml3d_align_train_val/ rm -rf motionx/motion_data/smplx_322/humanml3d_align_test rm -rf motionx/texts/semantic_labels/humanml3d_align_test mv motionx/motion_data/smplx_322/humanml3d_align_train_val motionx/motion_data/smplx_322/humanml mv motionx/texts/semantic_labels/humanml3d_align_train_val motionx/texts/semantic_labels/humanmlNoted: As described in our paper, our base model has two variants: MotionCraft-Basic and MotionCraft-Mix. MotionCraft-Basic refers to the model where the first stage is trained only on the HumanML3D subset of MC-Bench, while MotionCraft-Mix refers to the model where the first stage is trained on all subsets of MC-Bench (HumanML3D, BEAT2, FineDance). The text processing components for the latter two are included in the Speech-to-Motion Benchmark Construction and Music-to-Dance Benchmark Construction.
Speech-to-Gesture Benchmark Construction
- Clone the original BEAT2 dataset and unzip it in data/datasets/beats2/PantoMatrix:
cd ./data/datasets/beats2 git clone https://github.com/PantoMatrix/PantoMatrix.git cd PantoMatrix git lfs install git clone https://huggingface.co/datasets/H-Liu1997/BEAT2 unzip beat_v2.0.0.zip - Prepare relevant statistical metrics:
mv ./data/datasets/beats2/mean.npy ./data/datasets/beats2/PantoMatrix/ mv ./data/datasets/beats2/std.npy ./data/datasets/beats2/PantoMatrix/
Music-to-Dance Benchmark Construction
- Download and organize the data as FineDance:
- label_json: contains the song name, coarse style, and fine-grained genre.
- motion: contains the SMPLH format motion data.
- music_wav: contains the music data in 'wav' format.
- music_npy: contains the music feature extracted by librosa follow AIST++
- Transform the original SMPL-H FineDance data into data in the SMPL-X format:
cd ./data/datasets/finedance python pre_motion.py
Evaluation Checkpoints Preparation
- Download the
distilbert/distilbert-base-uncased:cd ./data/evaluators/smplx322 git lfs install git clone https://huggingface.co/distilbert/distilbert-base-uncased - Download our pre-trained text-encoder and motion encoder and put them under here for evaluation.
Model Checkpoints
We have prepared three checkpoints for Text-to-Motion, Speech-to-Gesture and Music-to-Dance respectively. You can do the following steps to place the ckpts in the right directory and complete the evaluation and visualization.
# Dowloading
mkdir outputs
gdown –folder https://drive.google.com/drive/folders/1cY7JFtmqBEsI2R_UKcIzxcsLETw1OXwF
# Evaluation
bash tools/single_test.sh
bash tools/s2g_test.sh
bash tools/m2d_test.sh
# Visualization
bash tools/visualize.sh
bash tools/m2d_visualize.sh
bash tools/s2g_visualize.sh
Noted:
- For ease of use, the provided checkpoints for S2G and M2D have merged both the control branch and the main network. The backbone of the M2D checkpoint is the T2M checkpoint provided. For S2G, we observed that increasing the number of parameters helps stabilize performance, so a version with more parameters is provided.
- The naming convention of different checkpoints, such as epoch_x.pth, indicates the number of training epochs for each subtask. Variations in numbers arise from differences in task difficulty and dataset size.
You can evaluate using the script:
# text-to-motion
bash ./tools/single_test.sh
# speech-to-gesture
bash ./tools/s2g_test.sh
# music-to-dance
bash ./tools/m2d_test.sh
Noted: For detailed usage of the parameters, please refer to test.py, s2g_test.py, and m2d_test.py respectively.
You can inference with the script to get the corresponding visualization:
# text-to-motion
bash ./tools/visualize.sh
# speech-to-gesture
bash ./tools/s2g_visualize.sh
# music-to-dance
bash ./tools/m2d_visualize.sh
Noted: For detailed usage of the parameters, please refer to visualize.py, s2g_visualize.py, and m2d_visualize.py respectively. You can also refer to the TEMOS for the Blender visualization.
The code is coming soon!
@article{bian2024motioncraft,
title={MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls},
author={Bian, Yuxuan and Zeng, Ailing and Ju, Xuan and Liu, Xian and Zhang, Zhaoyang and Liu, Wei and Xu, Qiang},
journal={arXiv preprint arXiv:2407.21136},
year={2024}
}
Our code is mainly modified based on FineMoGen, and we refer to the HumanTomato for OpenTMA evaluation pipelines. Thanks to all the authors!
