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.

翻译：随着数字化的推进，可控三维人体运动生成已成为重要研究方向。现有方法虽得益于扩散模型的发展取得显著进展，但严重依赖精细捕捉和标注（如文本标注）的高质量运动数据——这一过程在现实应用中成本高昂。为此，我们提出MotionMix模型，这是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未标注的运动序列。具体而言，我们将扩散模型的去噪目标分解为两个阶段：通过前$T-T^*$步学习含噪声标注运动，获取条件粗粒度运动近似；随后利用未标注运动，在最后$T^*$步对这些初步运动进行无监督精修。值得强调的是，尽管使用两类不完美数据源，我们的模型在运动生成质量上并未妥协——与使用黄金标准数据的全监督方法相比表现相当。在多个基准数据集上的大量实验表明，作为通用框架的MotionMix在文本到运动、动作到运动、音乐到舞蹈任务中均持续达到最先进性能。