/TM2T

Official implementation of "TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts (ECCV2022)"

Primary LanguagePythonMIT LicenseMIT

TM2T: Stochastical and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts (ECCV 2022)

[Project Page] [Paper]

teaser_image

Python Virtual Environment

Anaconda is recommended to create this virtual environment.

conda create -f environment.yaml
conda activate tm2t

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

Python = 3.7.9   # Other version may also work but is 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
nlpeval (https://github.com/Maluuba/nlg-eval)     # For evaluation of motion-to-text only
bertscore (https://github.com/Tiiiger/bert_score) # For evaluation of motion-to-text only

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_KLD005/                   # A dumb folder containing information for evaluation dataloading
./checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/  # Motion discretizer
./checkpoints/t2m/M2T_EL4_DL4_NH8_PS/              # Motion (token)-to-Text translation model
./checkpoints/t2m/T2M_Seq2Seq_NML1_Ear_SME0_N/     # Text-to-Motion (token) generation 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 discretizer

HumanML3D

python train_vq_tokenizer_v3.py --gpu_id 0 --name VQVAEV3_CB1024_CMT_H1024_NRES3 --dataset_name t2m --n_resblk 3

KIT-ML

python train_vq_tokenizer_v3.py --gpu_id 0 --name VQVAEV3_CB1024_CMT_H1024_NRES3 --dataset_name kit --n_resblk 3

Tokenizing all motion data for the following training

HumanML3D

python tokenize_script.py --gpu_id 0 --name VQVAEV3_CB1024_CMT_H1024_NRES3 --dataset_name t2m

KIT-ML

python tokenize_script.py --gpu_id 0 --name VQVAEV3_CB1024_CMT_H1024_NRES3 --dataset_name kit

Training motion2text model:

HumanML3D

python train_m2t_transformer.py --gpu_id 0 --name M2T_EL4_DL4_NH8_PS --n_enc_layers 4 --n_dec_layers 4 --proj_share_weight --dataset_name t2m

KIT-ML

python train_m2t_transformer.py --gpu_id 0 --name M2T_EL3_DL3_NH8_PS --n_enc_layers 3 --n_dec_layers 3 --proj_share_weight --dataset_name kit

Training text2motion model:

HumanML3D

python train_t2m_joint_seq2seq.py --gpu_id 0 --name T2M_Seq2Seq_NML1_Ear_SME0_N --start_m2t_ep 0 --dataset_name t2m

KIT-ML

python train_t2m_joint_seq2seq.py --gpu_id 0 --name T2M_Seq2Seq_NML1_Ear_SME0_N --start_m2t_ep 0 --dataset_name kit

Motion & text feature extractors:

We use the same extractors provided by https://github.com/EricGuo5513/text-to-motion

Generating and Animating 3D Motions (HumanML3D)

Translating motions into langauge (using test set)

With Beam Search:

python evaluate_m2t_transformer.py --name M2T_EL4_DL4_NH8_PS --gpu_id 2 --num_results 20 --n_enc_layers 4 --n_dec_layers 4 --proj_share_weight --ext beam_search

With Sampling:

python evaluate_m2t_transformer.py --name M2T_EL4_DL4_NH8_PS --gpu_id 2 --num_results 20 --n_enc_layers 4 --n_dec_layers 4 --proj_share_weight --sample --top_k 3 --ext top_3

Generating motions from texts (using test set)

python evaluate_t2m_seq2seq.py --name T2M_Seq2Seq_NML1_Ear_SME0_N --num_results 10 --repeat_times 3 --sample --ext sample

where --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/T2M_Seq2Seq_NML1_Ear_SME0_N/sample/.

Sampling results from customized descriptions

python gen_script_t2m_seq2seq.py --name T2M_Seq2Seq_NML1_Ear_SME0_N  --repeat_times 3 --sample --ext customized --text_file ./input.txt

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

Evaluating Motion2Text

python final_evaluation_m2t.py

Evaluating Motion2Text

python final_evaluation_t2m.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 ./m2t(t2m)_evaluation.log.

Misc

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