Text-driven Motion Generation

Installation
Training
Acknowledgement

Installation

Please refer to install.md for detailed installation.

Training

Due to the requirement of a large batchsize, we highly recommend you to use DDP training. A slurm-based script is as below:

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITION} -n8 --gres=gpu:8 -u \
    python -u tools/train.py \
    --name t2m_sample \
    --batch_size 128 \
    --times 200 \
    --num_epochs 50 \
    --dataset_name t2m \
    --distributed

Otherwise, you can run the training code on a single GPU like:

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -u tools/train.py \
    --name t2m_sample \
    --batch_size 128 \
    --times 200 \
    --num_epochs 50 \
    --dataset_name t2m

Evaluation

# GPU_ID indicates which gpu you want to use
python -u tools/evaluation.py checkpoints/kit/kit_motiondiffuse/opt.txt GPU_ID
# Or you can omit this option and use cpu for evaluation
python -u tools/evaluation.py checkpoints/kit/kit_motiondiffuse/opt.txt

Visualization

You can visualize human motion with the given language description and the expected motion length. We also provide a Colab Demo and a Hugging Face Demo for your convenience.

# Currently we only support visualization of models trained on the HumanML3D dataset. 
# Motion length can not be larger than 196, which is the maximum length during training
# You can omit `gpu_id` to run visualization on your CPU

python -u tools/visualization.py \
    --opt_path checkpoints/t2m/t2m_motiondiffuse/opt.txt \
    --text "a person is jumping" \
    --motion_length 60 \
    --result_path "test_sample.gif" \
    --gpu_id 0

Here are some visualization examples. The motion lengths are shown in the title of animations.