Sign language (SL) serves as a vital mode of communication for millions of individuals worldwide. However, current SL avatar reconstruction systems face significant computational inefficiencies that hinder their deployment in real-time and resource-constrained environments. SGNify, a leading SL avatar synthesis framework, achieves high-fidelity reconstructions by processing every frame of a video input. However, this exhaustive approach results in considerable redundancy due to static or low-motion frames, leading to unnecessary computational overhead. This manuscript introduces a novel optimization framework called FESLAR (Frame-Efficient Sign Language Avatar Reconstruction). FESLAR uses motion-based thresholding to identify linguistically critical frames, which are then processed through SGNify to generate high-fidelity 3D avatar meshes. FESLAR utilizes the FILM model for video frame generation and RBF mesh interpolation for 3D avatar continuity. Quantitative evaluations across multiple SL video sequences demonstrate that FESLAR achieves up to 84% reduction in computational workload with minimal loss in visual quality, as measured by SSIM, RMSE, and mesh deformation metrics. This hybrid strategy balances efficiency and expressiveness while enabling scalable applications in education, live interpretation, and assistive technologies, and offers a significant advancement toward real-time, accessible sign language communication systems.