  The target duration of a synthesized human motion is a critical attribute
that requires modeling control over the motion dynamics and style. Speeding up
an action performance is not merely fast-forwarding it. However,
state-of-the-art techniques for human behavior synthesis have limited control
over the target sequence length.
  We introduce the problem of generating length-aware 3D human motion sequences
from textual descriptors, and we propose a novel model to synthesize motions of
variable target lengths, which we dub "Length-Aware Latent Diffusion" (LADiff).
LADiff consists of two new modules: 1) a length-aware variational auto-encoder
to learn motion representations with length-dependent latent codes; 2) a
length-conforming latent diffusion model to generate motions with a richness of
details that increases with the required target sequence length. LADiff
significantly improves over the state-of-the-art across most of the existing
motion synthesis metrics on the two established benchmarks of HumanML3D and
KIT-ML.
