K-Mask: Kinematic-Aware Masked Modeling for Controllable Text-to-Motion Synthesis

Masked generative models have made significant advancesin text-to-motion synthesis. Previous approaches adopt a non-factorized,whole-body tokenization, limiting compositional and fine-grained con-trol. We present K-Mask, a kinematic-aware generative framework thatfactors motion into anatomically grounded groups and learns temporaland cross-group interactions through masked modeling. In the proposedapproach, a kinematic-group residual VQ-VAE (KG-RVQ) encodes mo-tion into disentangled, group-aligned latents. In particular, a latent-awarekinematic dropout (LAKD) loss is proposed to promote within-groupreconstruction and suppress inter-group leakage. Then, we introduce twobidirectional spatiotemporal transformers: (i) a base model that generatescoarse tokens over a time-group lattice with axis-factorized attention, and(ii) a residual model that predicts deeper quantizer layers for refinement.Finally, We introduce kinematic group masking, which randomly occludesentire group-specific subtrees, to enhance inter-group reasoning. On theHumanML3D and KIT-ML text-to-motion benchmarks, K-Mask obtainscompetitive FID scores of 0.041 and 0.154, respectively, while providingan anatomically factorized token interface for localized motion control.