Human motion generation goes image diffusion: joint trajectories become images, and two skeletons share one model
Text-to-motion generation no longer needs a separate model per skeleton: once root-centered joint trajectories are arranged into joint-time images, image diffusion architectures share one motion autoencoder and one generator across HumanML3D and KIT-ML while keeping each dataset's native skeletal definition.