HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models
  with Minimal Feedback
Gaoge Han Shaoli Huang Mingming Gong Jinglei Tang
We introduce HuTuMotion, an innovative approach for generating natural human
motions that navigates latent motion diffusion models by leveraging few-shot
human feedback. Unlike existing approaches that sample latent variables from a
standard normal prior distribution, our method adapts the prior distribution to
better suit the characteristics of the data, as indicated by human feedback,
thus enhancing the quality of motion generation. Furthermore, our findings
reveal that utilizing few-shot feedback can yield performance levels on par
with those attained through extensive human feedback. This discovery emphasizes
the potential and efficiency of incorporating few-shot human-guided
optimization within latent diffusion models for personalized and style-aware
human motion generation applications. The experimental results show the
significantly superior performance of our method over existing state-of-the-art
approaches.