Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured and annotated (e.g., text) high-quality motion corpus, a resource-intensive endeavor in the real world. This motivates our proposed MotionMix, a simple yet effective weakly-supervised diffusion model that leverages both noisy and unannotated motion sequences. Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining conditional rough motion approximations in the initial $T-T^*$ steps by learning the noisy annotated motions, followed by the unconditional refinement of these preliminary motions during the last $T^*$ steps using unannotated motions. Notably, though learning from two sources of imperfect data, our model does not compromise motion generation quality compared to fully supervised approaches that access gold data. Extensive experiments on several benchmarks demonstrate that our MotionMix, as a versatile framework, consistently achieves state-of-the-art performances on text-to-motion, action-to-motion, and music-to-dance tasks. Project page: https://nhathoang2002.github.io/MotionMix-page/

翻译：随着世界迈向数字化转型，三维人体运动的可控生成已成为重要研究课题。现有方法虽借助扩散模型取得了显著进展，但严重依赖精心采集并标注（如文本注释）的高质量运动数据集——这在现实应用中需要耗费大量资源。为此，我们提出MotionMix，一种简单而有效的弱监督扩散模型，能够同时利用含噪声和无标注的运动序列。具体而言，我们将扩散模型的去噪目标分为两个阶段：在前$T-T^*$步中，通过学习含噪声的标注运动获取条件粗略运动近似；随后在最后$T^*$步中，利用无标注运动对初步结果进行无条件优化。值得注意的是，尽管从两类非完美数据中学习，我们的模型在运动生成质量上仍可与使用黄金数据的全监督方法相媲美。在多个基准测试上的大量实验表明，作为通用框架，MotionMix在文本到运动、动作到运动及音乐到舞蹈任务中均持续取得最先进性能。项目主页：https://nhathoang2002.github.io/MotionMix-page/