MotionLCM: Real-time Controllable Motion Generation via Latent
  Consistency Model
Authors: name: Wenxun Dai, name: Ling-Hao Chen, name: Jingbo Wang, name: Jinpeng Liu, name: Bo Dai, name: Yansong Tang

This work introduces MotionLCM, extending controllable motion generation to a
real-time level. Existing methods for spatial control in text-conditioned
motion generation suffer from significant runtime inefficiency. To address this
issue, we first propose the motion latent consistency model (MotionLCM) for
motion generation, building upon the latent diffusion model (MLD). By employing
one-step (or few-step) inference, we further improve the runtime efficiency of
the motion latent diffusion model for motion generation. To ensure effective
controllability, we incorporate a motion ControlNet within the latent space of
MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the
vanilla motion space to control the generation process directly, similar to
controlling other latent-free diffusion models for motion generation. By
employing these techniques, our approach can generate human motions with text
and control signals in real-time. Experimental results demonstrate the
remarkable generation and controlling capabilities of MotionLCM while
maintaining real-time runtime efficiency.