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We introduce HuTuMotion, an innovative approach for generating natural humanmotions that navigates latent motion diffusion models by leveraging few-shothuman feedback. Unlike existing approaches that sample latent variables from astandard normal prior distribution, our method adapts the prior distribution tobetter suit the characteristics of the data, as indicated by human feedback,thus enhancing the quality of motion generation. Furthermore, our findingsreveal that utilizing few-shot feedback can yield performance levels on parwith those attained through extensive human feedback. This discovery emphasizesthe potential and efficiency of incorporating few-shot human-guidedoptimization within latent diffusion models for personalized and style-awarehuman motion generation applications. The experimental results show thesignificantly superior performance of our method over existing state-of-the-artapproaches.

HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback