This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and data annotation. We propose a transformer-based architecture that models the temporal dynamics of signing and frames segmentation as a sequence labeling problem using the Begin-In-Out (BIO) tagging scheme. Our method leverages the HaMeR hand features, and is complemented with 3D Angles. Extensive experiments show that our model achieves state-of-the-art results on the DGS Corpus, while our features surpass prior benchmarks on BSLCorpus.
@article{arxiv.2504.08593,
title = {Hands-On: Segmenting Individual Signs from Continuous Sequences},
author = {JianHe Low and Harry Walsh and Ozge Mercanoglu Sincan and Richard Bowden},
journal= {arXiv preprint arXiv:2504.08593},
year = {2026}
}
Comments
Accepted in the 19th IEEE International Conference on Automatic Face and Gesture Recognition. Code Implementation Released