# SRV Quantizer

Sign language generation turns written sentences into continuous signing motion, which requires deciding which signs to express and then realizing handshape, trajectory and body coordination. Existing pose-based systems and reconstruction-only motion tokenizers entangle those two decisions, leaving the generator without a text-aligned intermediate target. SRVQ builds a hierarchy inside a single residual quantizer: the first codebook is supervised by text and gloss to encode sign content, later residual levels add kinematic detail, and a semantic generator followed by a kinematic generator predicts the codes. On CSL-Daily and Phoenix-2014T the full variant reaches retrieval R@1 of 82.31 and 70.25, versus 51.96 and 24.14 for SOKE, gains of 30.35 and 46.11 points.

Sign language generation turns written sentences into continuous signing motion, which requires deciding which signs to express and then realizing handshape, trajectory and body coordination. Existing pose-based systems and reconstruction-only motion tokenizers entangle those two decisions, leaving the generator without a text-aligned intermediate target. SRVQ builds a hierarchy inside a single residual quantizer: the first codebook is supervised by text and gloss to encode sign content, later residual levels add kinematic detail, and a semantic generator followed by a kinematic generator predicts the codes. On CSL-Daily and Phoenix-2014T the full variant reaches retrieval R@1 of 82.31 and 70.25, versus 51.96 and 24.14 for SOKE, gains of 30.35 and 46.11 points.