Author Identifier (ORCID)
Abstract
Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence generator that translates HamNoSys notation into two-dimensional human pose sequences. Our framework makes two complementary contributions. First, we introduce a coarse-to-fine generation strategy with multi-scale supervision: the model is first guided by an intermediate body–hand–face scaffold to encourage global structural coherence, and then refines fine-grained hand articulation to improve finger-level detail. Second, we investigate integrating Kolmogorov–Arnold Network modules into a Transformer backbone, using learnable univariate function primitives to model the highly non-linear mapping from discrete phonological symbols to continuous body kinematics with a compact parameterization. Experiments on multiple public corpora spanning Polish, German, Greek, and French sign languages show consistent reductions in dynamic time warping based joint error compared with a strong notation-to-pose baseline, while using substantially fewer parameters. Controlled ablations further indicate that KAN-based variants substantially reduce parameter count while maintaining competitive performance when coupled with multi-scale supervision, rather than serving as the main driver of accuracy gains. These findings position multi-scale supervision as the key mechanism for improving notation-conditioned pose generation, with KAN offering a compact alternative for efficient modeling. Our code will be publicly available.
Keywords
human pose animation, Kolmogorov-Arnold networks, sign language notation
Document Type
Journal Article
Date of Publication
9-14-2026
Volume
694
Publication Title
Neurocomputing
Publisher
Elsevier
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Du, G., Wang, L., Hu, K., & Wang, Z. (2026). KANMultiSign: Multi-scale sequence-based pose animation from sign language notation with Kolmogorov-Arnold networks. Neurocomputing, 694, 133930. https://doi.org/10.1016/j.neucom.2026.133930