A Closer Look at Skeleton-based Continuous Sign Language Recognition

Yuecong Min, Yifan Yang, Peiqi Jiao, Zixi Nan, Xilin Chen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 4968-4974

Skeleton-based Sign Language Recognition (SLR) has emerged as a promising alternative to video-based approaches, offering robustness to visual noise, enhanced privacy, and reduced computational overhead, making it suitable for real-world deployment. However, effectively leveraging skeleton data and improving the generalization ability of skeleton-based continuous SLR methods remain open challenges. In this work, we conduct a comprehensive study of skeleton-based continuous SLR, focusing on input representations, preprocessing strategies, architectural designs, and cross-dataset transferability. The proposed method achieves Top-1 performance on both signer-independent recognition and unseen sentence generalization under the MSLR Track-1 challenge. Experimental results not only validate the potential of skeleton-based models but also provide practical insights for designing effective and efficient SLR systems. Our codes will be available at https://github.com/VIPL-SLP/MSLR_ICCV2025.