Inter-Joint Relations Matter: Asymmetric Motion–Distance Denoising for Text-to-Motion Generation

In text-to-motion generation, many described actions hinge on changing relations between body parts, such as the hands meeting at the right moment during clapping. However, most generators leave these relations implicit in joint states or motion codes, using joint-derived distances, if at all, only as losses or sampling guidance, so the denoiser has no explicit relational state to follow. We propose Asymmetric Motion–Distance Denoising (AMDD), which jointly denoises motion and sparse, continuous inter-joint distances. Its two-stream denoiser couples them one way: shielded from noisy motion, the distance stream gives the motion stream a stable relational reference at every denoising step. A geometric consistency objective encourages the predicted motion to realize the predicted distances, and a text-conditioned gate scales the distance stream's updates. AMDD lowers FID by 43–84% on HumanML3D across three diffusion backbones and by 12% on SnapMoGen, and its distance stream reduces inter-joint distance error about twice as much as position error.