  In motion generation, controllability as well as generation quality and speed
is becoming more and more important. There are various motion editing tasks,
such as in-betweening, upper body editing, and path-following, but existing
methods perform motion editing with a data-space diffusion model, which is slow
in inference compared to a latent diffusion model. In this paper, we propose
MoLA, which provides fast and high-quality motion generation and also can deal
with multiple editing tasks in a single framework. For high-quality and fast
generation, we employ a variational autoencoder and latent diffusion model, and
improve the performance with adversarial training. In addition, we apply a
training-free guided generation framework to achieve various editing tasks with
motion control inputs. We quantitatively show the effectiveness of adversarial
learning in text-to-motion generation, and demonstrate the applicability of our
editing framework to multiple editing tasks in the motion domain.
