CMDer: Controllable Mode Decomposition-Based Single Motion Synthesis with Diffusion

Realistic motion generation from a single example remainschallenging due to the scarcity of large-scale, high-quality motion datasets,particularly for animals and custom characters. Existing approaches mainlyoperate in temporal or spatial domains, where controllability is imposedthrough joint-level or time-based constraints. However, motion signalsexhibit a natural hierarchical structure across frequency scales: low-frequency components encode the fundamental rhythm and movementpattern, while high-frequency components capture individual details andvariations. This observation suggests an alternative control dimensionbased on frequency-selective motion synthesis. We present CMDer (pro-nounced Commander), a Controllable Mode Decomposition-based SingleMotion Synthesis framework that leverages diffusion models to generatediverse motions from a single motion example. CMDer employs Multi-variate Variational Mode Decomposition (MVMD) to represent motionsequences as intrinsic frequency modes, providing an interpretable andstructured representation for motion synthesis. A lightweight U-Net dif-fusion model then learns to generate these modes, while a Mode En-hancement Block (MEB) improves local motion modeling. By perform-ing diffusion directly in the decomposed motion modes, CMDer enablesfrequency-selective control that allows users to preserve global rhythmicpatterns while varying fine-grained motion details. Experiments demon-strate that CMDer achieves generation quality comparable to state-of-the-art temporal-domain approaches while introducing a new control-lable dimension for motion synthesis. We further showcase diverse appli-cations including mode-based synthesis, cross-modality motion genera-tion, and controllable crowd animation.