###### Abstract  
In this work, we introduce IK-AUG, a unified algorithmic framework for kinematics-driven data augmentation tailored to sign language recognition (SLR). Departing from traditional augmentation techniques that operate at the pixel or feature level, our method integrates inverse kinematics (IK) and virtual simulation to synthesize anatomically valid gesture sequences within a structured 3D environment. The proposed system begins with sparse 3D keypoints extracted via a pose estimator and projects them into a virtual coordinate space. A differentiable IK solver based on forward-and-backward constrained optimization is then employed to reconstruct biomechanically plausible joint trajectories. To emulate natural signer variability and enhance data richness, we define a set of parametric perturbation operators spanning spatial displacement, depth modulation, and solver sensitivity control. These operators are embedded into a generative loop that transforms each original gesture sample into a diverse sequence cluster, forming a high-fidelity augmentation corpus. We benchmark our method across five deep sequence models (CNN3D, TCN, Transformer, Informer, and Sparse Transformer) and observe consistent improvements in accuracy and convergence. Notably, Informer achieves 94.1% validation accuracy with IK-AUG enhanced training, underscoring the framework's efficacy. These results suggest that algorithmic augmentation via kinematic modeling offers a scalable, annotation free pathway for improving SLR systems and lays the foundation for future integration with multi-sensor inputs in hybrid recognition pipelines. caption = title]Integr  
Moreover, traditional data augmentation techniques such as image-level transformations or synthetic recombination fail to capture the biomechanical realism and temporal coherence intrinsic to human gestures [11; 12]. As a result, models trained on such data often struggle with signer variability, camera-view inconsistencies, and gesture ambiguity in practical applications [13]. To address these challenges, we propose a unified algorithmic framework, Inverse Kinematics-Augmented Gesture Generator (IK-AUG), which systematically synthesizes biomechanically plausible sign language sequences through kinematic modeling and structured simulation. Instead of relying on pixel-level transformations or stochastic recombination, IK-AUG formulates sign augmentation as a pose-to-motion reconstruction problem, where sparse 3D landmarks are lifted into full joint trajectories using an inverse kinematics solver embedded in a virtual environment. The core of IK-AUG integrates a differentiable kinematic engine based on forward-and-backward constraint propagation with a set of multi-modal perturbation operators parameterized by spatial noise, depth offsets, and solver sensitivity modulation. These operators act as domain-specific augmentation functions that preserve semantic intent while injecting controlled variability across anatomical and dynamic dimensions. To enable comprehensive evaluation, we instantiate IK-AUG within a Unity-based simulation pipeline and generate an enriched dataset from limited raw gestures. The synthesized samples are then used to train five representative temporal recognition models: CNN3D [14], Temporal Convolutional Network (TCN) [15], Transformer [16], Informer [17], and Sparse Transformer [18], revealing consistent accuracy improvements and faster convergence. Notably, attention-based models exhibit the most significant gains, validating the framework's ability to enhance generalization across spatiotemporal architectures. By transforming raw motion capture into high-fidelity gesture distributions, our algorithm not only mitigates data scarcity but also establishes a scalable, annotation-free augmentation paradigm for future gesture-based recognition systems.