This work focuses on skeleton-aware sign language recognition (SLR) which receives a series of skeleton points to classify the classes of sign language. Compared to RGB-based inputs, it consumes <1/3 computations and achieve >2× inference speed. This work proposes a dynamic spatial-temporal aggregation network for skeleton-aware sign language recognition. It achieves new state-of-the-art performance on four datasets including NMFs-CSL, SLR500, MSASL and WLASL and outperforms previous methods by a large margin.

The preprocessed skeleton data for NMFs-CSL, SLR500, MSASL and WLASL datasets are provided here. Please be sure to follow their rules and agreements when using the preprocessed data.

For datasets (WLASL100, WLASL300, WLASL1000, WLASL2000, MLASL100, MLASL200, MLASL500, MLASL1000, SLR500, NMFs-CSL) used to train or test our model, first create a soft link. For example, for WLASL2000:

ln -s path_to_your_WLASL2000/WLASL2000/ ./data/WLASL2000

We provide the pretrained weight for our model on the WLASL2000 dataset to validate its performance in ./pretrained_models

To install necessary packages, run this command.

pip install -r requirements.txt

Conduct the following commands:

mkdir save_models

python -u main.py --config config/train.yaml --device your_device_id

python -u main.py --config config/test.yaml --device your_device_id

To test your model with pretrained weights, you may modify the line 52 in ./config/test.yaml to path of your pretrained weight.

Update: There seems to be an error that loading pretrained models doesn't give correct inference results. This doesn't affect the normal training procedure.

This code is based on SAM-SLR-v2 and SLGTformer. Many thanks for the authors for open sourcing their code.