  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/
