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.

翻译：我们提出HuTuMotion，一种通过利用少样本人类反馈来导航潜运动扩散模型以生成自然人体运动的创新方法。与现有方法从标准正态先验分布中采样潜变量不同，我们的方法根据人类反馈调整先验分布，使其更适应数据特征，从而提升运动生成质量。此外，我们的发现表明，利用少样本反馈能够达到与大量人类反馈相当的性能水平。这一发现强调了在潜扩散模型中引入少样本人类引导优化用于个性化和风格感知人体运动生成应用的潜力与效率。实验结果表明，我们的方法在性能上显著优于现有最先进方法。