Year: 2026
Type: conference-paper
Abstract: Sign language corpora face a structural tension between open-access requirements and the irreducible biometric identity embedded in visual, gestural data.While 3D pose estimation enables signer-agnostic abstraction, the representational adequacy of pose-based modeling for preserving linguistic structure remains underexplored.This paper introduces a perceptually-grounded kinematic modeling framework that formalizes 3D landmark sequences as an intermediate linguistic representation and validates their adequacy through avatar-mediated synthesis and large-scale human evaluation.Using 30370 gloss-level Kenyan Sign Language (KSL) segments derived from the AI4KSL corpus, we construct normalized 3D motion trajectories via MediaPipe Holistic.These trajectories are retargeted to parameterized avatars through a constrained kinematic mapping that preserves non-manual marker geometry and articulatory timing.We define a dual evaluation paradigm combining geometric fidelity metrics (PCK=92.7%,OKS=0.88,PCP=91.5%,PDJ>85.3%) with perceptual constructs measured across a statistically powered Deaf participant cohort (N=384).Results demonstrate a strong predictive relationship between structural joint precision and perceived gesture clarity (r=0.76,p<.01), suggesting that linguistic adequacy is partially recoverable from normalized kinematic structure.Furthermore, representational diversity in avatar instantiation significantly increases perceived inclusivity without degrading intelligibility.These findings establish pose-based motion abstraction not merely as an anonymization technique but as a viable corpus-level modeling layer for ethically sustainable language in motion.
Authors: Ezekiel Maina, Lilian Wanzare, James Obuhuma
Institution: Maseno University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166511474