  Conventional methods for human motion synthesis are either deterministic or
struggle with the trade-off between motion diversity and motion quality. In
response to these limitations, we introduce MoFusion, i.e., a new
denoising-diffusion-based framework for high-quality conditional human motion
synthesis that can generate long, temporally plausible, and semantically
accurate motions based on a range of conditioning contexts (such as music and
text). We also present ways to introduce well-known kinematic losses for motion
plausibility within the motion diffusion framework through our scheduled
weighting strategy. The learned latent space can be used for several
interactive motion editing applications -- like inbetweening, seed
conditioning, and text-based editing -- thus, providing crucial abilities for
virtual character animation and robotics. Through comprehensive quantitative
evaluations and a perceptual user study, we demonstrate the effectiveness of
MoFusion compared to the state of the art on established benchmarks in the
literature. We urge the reader to watch our supplementary video and visit
https://vcai.mpi-inf.mpg.de/projects/MoFusion.
