  Gloss-free Sign Language Production (SLP) offers a direct translation of
spoken language sentences into sign language, bypassing the need for gloss
intermediaries. This paper presents the Sign language Vector Quantization
Network, a novel approach to SLP that leverages Vector Quantization to derive
discrete representations from sign pose sequences. Our method, rooted in both
manual and non-manual elements of signing, supports advanced decoding methods
and integrates latent-level alignment for enhanced linguistic coherence.
Through comprehensive evaluations, we demonstrate superior performance of our
method over prior SLP methods and highlight the reliability of Back-Translation
and Fr\'echet Gesture Distance as evaluation metrics.
