KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation

Researchers introduce KANMultiSign, a neural network framework that converts sign language notation into pose animations using Kolmogorov-Arnold Networks integrated with Transformers. The system achieves improved accuracy with fewer parameters across multiple sign languages, demonstrating that multi-scale supervision is the key driver of performance gains.

This research represents a meaningful advancement in accessibility technology rather than a direct cryptocurrency or financial market development. The work combines two emerging computational approaches—Kolmogorov-Arnold Networks (KANs) and multi-scale sequence generation—to solve a practical problem in sign language accessibility. The significance lies in demonstrating that KANs, a relatively novel neural architecture gaining attention in machine learning circles, can be effectively integrated into practical applications with measurable efficiency gains.

The accessibility sector has struggled with scalable solutions for sign language production, traditionally requiring expensive human animators or limited rule-based systems. KANMultiSign addresses this by leveraging symbolic notation (HamNoSys) as input, creating a pathway toward automated, high-quality sign animation across multiple languages. The coarse-to-fine generation strategy—progressively refining from body structure to hand articulation to finger detail—mirrors approaches successful in other vision and sequence modeling tasks.

The technical contributions have broader implications for the AI community. The finding that multi-scale supervision drives performance gains more than KAN architecture itself is a valuable empirical insight. This challenges assumptions about architectural novelty and refocuses attention on training methodology and data structure. For developers building accessibility tools, the demonstrated parameter efficiency (fewer weights with competitive results) offers practical benefits for deployment on resource-constrained devices.

Looking forward, the public code release will likely accelerate adoption in accessibility applications. The multi-language validation across Polish, German, Greek, and French sign languages suggests the approach generalizes beyond English-centric AI systems. This work exemplifies how fundamental AI research translates into inclusive technology with tangible human benefits.

• →KANMultiSign converts sign language notation to 2D pose sequences with fewer parameters than baseline models across multiple languages
• →Multi-scale supervision with body-hand-face scaffolding proved more impactful than KAN architecture for performance improvements
• →The system demonstrates consistent reductions in joint error using dynamic time warping metrics on Polish, German, Greek, and French sign languages
• →Kolmogorov-Arnold Networks offer compact parameterization for modeling non-linear phonological-to-kinematic mappings
• →Public code release will enable broader adoption in accessible sign language animation tools