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. Additionally, HuTuMotion also supports personalized and style-aware motion generation. The experimental results show the significantly superior performance of our method over existing state-of-the-art approaches. 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 human motion generation applications.

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

Main Track - Computer Vision

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Gaoge Han

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

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