  Text-based motion generation models are drawing a surge of interest for their
potential for automating the motion-making process in the game, animation, or
robot industries. In this paper, we propose a diffusion-based motion synthesis
and editing model named FLAME. Inspired by the recent successes in diffusion
models, we integrate diffusion-based generative models into the motion domain.
FLAME can generate high-fidelity motions well aligned with the given text.
Also, it can edit the parts of the motion, both frame-wise and joint-wise,
without any fine-tuning. FLAME involves a new transformer-based architecture we
devise to better handle motion data, which is found to be crucial to manage
variable-length motions and well attend to free-form text. In experiments, we
show that FLAME achieves state-of-the-art generation performances on three
text-motion datasets: HumanML3D, BABEL, and KIT. We also demonstrate that
editing capability of FLAME can be extended to other tasks such as motion
prediction or motion in-betweening, which have been previously covered by
dedicated models.
