EMS: Elaborative Motion Synthesis

Introduction

This is the official implementation of Breaking The Limits of Text-conditioned 3D Motion Synthesis with Elaborative Descriptions.

Please contact [email protected] if you have problem when using this code repo.

Environmental Setup

Install Packages

Please use the environment.yaml file to install required packages.

    conda env create -f environment.yaml

Download Text Dependency

Just Run:

cd deps/
git lfs install
git clone https://huggingface.co/distilbert-base-uncased
cd ../

Download Body Model

Please register and download the SMPLH body model from here, and follow the introductions in TEMOS to preprocess the files. Finally, you should get a directory as below:

${ROOT}  
|-- deps
|   |-- smplh
|   |   |-- SMPLH_FEMALE.npz
|   |   |-- SMPLH_MALE.npz
|   |   |-- SMPLH_NEUTRAL.npz
|   |   |-- smplh.faces

Install Blender (Optional)

This is only needed if you want to render the 3D human figures. We use blender to render the SMPL sequence, please install it from here. We build and test the code on blender 2.93, but higher version may also work.

Data Preparation

Please download BABEL annotation files from BABEL unzip and replace the "$path_to_BABEL_FOLDER" to where the BABEL dataset is downladed. Please download SMPLH human motion data from AMASS. Currently, our model is trained with SMPLH body model, so please select the "SMPL+H G" icon in the download page. After downloading, please change the "$path_to_amass" to where the AMASS dataset is downloaded and "$path_to_extracted_feature_folder" to the place where you want to store the extracted features. Then run

mkdir datasets
cd util_tools
python prepare_annt.py --babel_path $path_to_BABEL_FOLDER --amass_path $path_to_amass
python preprocess.py --amass_path $path_to_amass --feat_folder $path_to_extracted_feature_folder
python prepare_clf.py --babel_path $path_to_BABEL_FOLDER
cd ../

You are expected to get the annotation file "babelsync.json" under "./datasets" and extracted feature files (xxx.pt) under your selected folder.

Evaluation

To quickly evaluate our model, please firstly follow the data preparation steps to get the converted annotation file and feature folder, then download the pretrained model from here, and download the action recognition model from here.

To evaluate the model with APE&AVE metrics, simply run:

    python eval_temos.py folder=$path_to_pretrained_model_folder

To evaluate the model with Acc&FID metrics, you will take three steps: Firstly, run

    python sample_clf.py folder=$path_to_pretrained_model_folder feat_save_dir=$path_to_sample_feat

to sample motion feature files with EMS.

Then, run

    cd util_tools
    python preprocess_clf.py --gt_feat_folder $path_to_extracted_feature_folder --feat_folder $path_to_sample_feat
    cd ../

to update the extracted ground truth feature path and generated feature path in the annotation file.

Finally, run

    python eval_clf.py folder=$path_to_action_recognition_model

to get the acc&fid metrics.

Training

To train the EMS model yourself, please also follow the data preparation steps to get the converted annotation file and feature folder, then download the humor prior model from here and place it under the "./outputs" folder.

Finally run the training script:

    python train.py data=babelsync-ems-mul-amass-rot run_id=iccv-new data.batch_size=8 model=ems model.optim.lr=5.0e-05 init_weight=./outputs/humor_prior.pt

Our experiments are made on 8 V100 GPUs with a total batch size of 8X8=64, so you may need to change the optim.lr accordingly based on the GPUs you used.

Interactive Rendering

To make it easier to use our model, we also provide an interactive code which takes in natural language descriptions and durations of each atomic action. Please modify the input information in "./input.json" then run (we provide several samples under "./texts"):

    python interact.py folder=./outputs/pretrained/ems

    blender --background --python render.py -- npy=./outputs/pretrained/ems/neutral_input/ems.npy mode=video

You are expected to get a ems.webv file under "./outputs/pretrained/ems/interact_samples/neutral_input".

Acknowledgments

We want to especially thank the following contributors that our code is based on: TEMOS,MDM, and MultiAct.

License

EMS is released under the MIT license.