Retrieving and Refining Winning Noise Tickets for Diffusion-Based Motion Generation

Diffusion-based text-to-motion models synthesize realistic hu-man motions but often exhibit semantic drift from the input text. Mo-tion is inherently temporal, especially in compositional and long-durationsequences that require semantic consistency across multiple action seg-ments and smooth kinematic transitions throughout the trajectory. Weposit that the initial noise is central to this consistency: within the Gaus-sian noise space, certain instances, i.e. winning noise tickets, carry latentstructure that biases denoising toward particular motion semantics, evenunder null prompts. We propose WInning Noise Retrieval and Optimiza-tion (WINRO), a training-free, model-agnostic framework that improvestext–motion alignment by selecting and refining such tickets before diffu-sion sampling. WINRO maps random noises to motion features generatedunder null prompts, retrieves the best-aligned noise for a given text, andrefines it via a KL-regularized objective that reduces the residual seman-tic gap while preserving the Gaussian prior. An optional LoRA-basedadapter amortizes this refinement into a single forward pass. WINROconsistently improves text–motion fidelity across different base models,MDM and MotionLCM, on HumanML3D without retraining, improvestemporal robustness on the MTT benchmark, and generalizes to appli-cations such as motion stylization and spatial constraint satisfaction.