Codebook-Geometry Correction: Replacing Test-Time Refinement with a Few Analytic Steps in Trajectory-Controlled Text-to-Motion Generation

Text-to-motion generators produce fluent, text-aligned motion, but steering a joint along a prescribed path usually costs either a duplicated control backbone or thousands of per-sample optimization steps at inference. We show that a handful of analytic steps can do the same job, provided the correction is measured in the right geometry. Our central contribution is Codebook-Geometry Correction (CodeGeo): the generator’s part codebooks record which changes its frozen decoder was trained to absorb, and so define a metric in which a correction can be measured; CodeGeo moves a generated clip onto the commanded path by the smallest change that metric allows, in at most eight damped steps in place of 1,600 refinement iterations. CodeGeo needs a start that is already close to the targets, which Key/Value Control (KV-Control) supplies: a thin interface (10.5 M trainable parameters) that feeds the trajectory into a frozen masked transformer as extra key/value memory at every attention layer, over Anatomy-Aware Part Tokenization (PartVQ), one codebook per body part unpacked so that every part-frame token is an addressable attention site. CodeGeo has one hyperparameter, the anisotropy of its step metric, and its setting is not free: it follows how far the commanded anchors spread across body parts, with single-part and body-spanning anchor sets preferring opposite settings in every configuration we tested. On pelvis control the method tracks to 0.24 cm at FID 0.067 in 1/21 the per-sample time of the iterative schedule it replaces; on multi-joint control it reaches 0.25 cm at FID 0.072, improving on MaskControl’s published 0.72 cm and 0.083 on both axes.