This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions. This is particularly efficient because evaluating the model's output is easier than gathering the motion that performs a desired task (e.g. backflip). To pioneer the exploration of this paradigm, we annotate 3,528 preference pairs generated by MotionGPT, marking the first effort to investigate various algorithms for learning from preference data. In particular, our exploration highlights important design choices when using preference data. Additionally, our experimental results show that preference learning has the potential to greatly improve current text-to-motion generative models. Our code and dataset are publicly available at https://github.com/THU-LYJ-Lab/InstructMotion}{https://github.com/THU-LYJ-Lab/InstructMotion to further facilitate research in this area.

该论文探讨了在文本到动作生成中的首选学习，指出当前的文本到动作生成仍依赖于需要具备专业知识的数据集和动作捕捉系统；而从人类偏好数据学习则不需要动作捕捉系统，只需没有专业知识的标注人员比较两个生成的动作。我们提供了3,528个由MotionGPT生成的首选对进行注释的数据集，标志着从偏好数据进行学习的首次尝试，并强调在使用偏好数据时的重要设计选择。此外，我们的实验结果表明偏好学习有着极大的潜力来改进当前的文本到动作生成模型。我们的代码和数据集已公开在https://github.com/THU-LYJ-Lab/InstructMotion以进一步促进该领域的研究。

探索人类偏好的文本生成动作