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Generating Diverse and Natural 3D Human Motions from Text (CVPR 2022)

[Project Page] [Paper]

Given a textual description for example, "the figure rises from a lying position and walks in a counterclockwise circle, and then lays back down the ground", our approach generates a diverse set of 3d human motions that are faithful to the provided text.

Python Virtual Environment

Anaconda is recommended to create this virtual environment.

conda create -f environment.yaml
conda activate text2motion_pub

If you cannot successfully create the environment, here is a list of required libraries:

Python = 3.7.9   # Other version may also work but are not tested.
PyTorch = 1.6.0 (conda install pytorch==1.6.0 torchvision==0.7.0 -c pytorch)  #Other version may also work but are not tested.
scipy
numpy
tensorflow       # For use of tensorboard only
spacy
tqdm
ffmpeg = 4.3.1   # Other version may also work but are not tested.
matplotlib = 3.3.1

After all, if you want to generate 3D motions from customized raw texts, you still need to install the language model for spacy.

python -m spacy download en_core_web_sm

Download Data & Pre-trained Models

If you just want to play our pre-trained models, you don't need to download datasets.

Datasets

We are using two 3D human motion-language dataset: HumanML3D and KIT-ML. For both datasets, you could find the details as well as download link [here].
Please note you don't need to clone that git repository, since all related codes have already been included in current git project.

Download and unzip the dataset files -> Create a dataset folder -> Place related data files in dataset folder:

mkdir ./dataset/

Take HumanML3D for an example, the file directory should look like this:

./dataset/
./dataset/HumanML3D/
./dataset/HumanML3D/new_joint_vecs/
./dataset/HumanML3D/texts/
./dataset/HumanML3D/Mean.mpy
./dataset/HumanML3D/Std.npy
./dataset/HumanML3D/test.txt
./dataset/HumanML3D/train.txt
./dataset/HumanML3D/train_val.txt
./dataset/HumanML3D/val.txt  
./dataset/HumanML3D/all.txt 

Pre-trained Models

Create a checkpoint folder to place pre-traine models:

mkdir ./checkpoints

Download models for HumanML3D from [here]. Unzip and place them under checkpoint directory, which should be like

./checkpoints/t2m/
./checkpoints/t2m/Comp_v6_KLD01/           # Text-to-motion generation model
./checkpoints/t2m/Decomp_SP001_SM001_H512/ # Motion autoencoder
./checkpoints/t2m/length_est_bigru/        # Text-to-length sampling model
./checkpoints/t2m/text_mot_match/          # Motion & Text feature extractors for evaluation

Download models for KIT-ML [here]. Unzip and place them under checkpoint directory.

Training Models

All intermediate meta files/animations/models will be saved to checkpoint directory under the folder specified by argument "--name".

Training motion autoencoder

HumanML3D

python train_decomp_v3.py --name Decomp_SP001_SM001_H512 --gpu_id 0 --window_size 24 --dataset_name t2m

KIT-ML

python train_decomp_v3.py --name Decomp_SP001_SM001_H512 --gpu_id 0 --window_size 24 --dataset_name kit

Train text2length model:

HumanML3D

python train_length_est.py --name length_est_bigru --gpu_id 0 --dataset_name t2m

KIT-ML

python train_length_est.py --name length_est_bigru --gpu_id 0 --dataset_name kit

Training text2motion model:

HumanML3D

python train_comp_v6.py --name Comp_v6_KLD01 --gpu_id 0 --lambda_kld 0.01 --dataset_name t2m

KIT-ML

python train_comp_v6.py --name Comp_v6_KLD005 --gpu_id 0 --lambda_kld 0.005 --dataset_name kit

Training motion & text feature extractors:

HumanML3D

python train_tex_mot_match.py --name text_mot_match --gpu_id 1 --batch_size 8 --dataset_name t2m

KIT-ML

python train_tex_mot_match.py --name text_mot_match --gpu_id 1 --batch_size 8 --dataset_name kit

Generating and Animating 3D Motions (HumanML3D)

Sampling results from test sets

python eval_comp_v6.py --name Comp_v6_KLD01 --est_length --repeat_time 3 --num_results 10 --ext default --gpu_id 1

where --est_length asks the model to use sampled motion lengths for generation, --repeat_time gives how many sampling rounds are carried out for each description. This script will results in 3x10 animations under directory ./eval_results/t2m/Comp_v6_KLD01/default/.

Sampling results from customized descriptions

python gen_motion_script.py --name Comp_v6_KLD01 --text_file input.txt --repeat_time 3 --ext customized --gpu_id 1

This will generate 3 animated motions for each description given in text_file ./input.txt.

If you find problem with installing ffmpeg, you may not be able to animate 3d results in mp4. Try gif instead.

Quantitative Evaluations

python final_evaluation.py 

This will evaluate the model performance on HumanML3D dataset by default. You could also run on KIT-ML dataset by uncommenting certain lines in ./final_evaluation.py. The statistical results will saved to ./t2m_evaluation.log.

Misc

Contact Chuan Guo at cguo2@ualberta.ca for any questions or comments.