  Motion diffusion models excel at text-driven motion generation but struggle
with real-time inference since motion sequences are time-axis redundant and
solving reverse diffusion trajectory involves tens or hundreds of sequential
iterations. In this paper, we propose a Motion Latent Consistency Training
(MLCT) framework, which allows for large-scale skip sampling of compact motion
latent representation by constraining the consistency of the outputs of
adjacent perturbed states on the precomputed trajectory. In particular, we
design a flexible motion autoencoder with quantization constraints to guarantee
the low-dimensionality, succinctness, and boundednes of the motion embedding
space. We further present a conditionally guided consistency training framework
based on conditional trajectory simulation without additional pre-training
diffusion model, which significantly improves the conditional generation
performance with minimal training cost. Experiments on two benchmarks
demonstrate our model's state-of-the-art performance with an 80\% inference
cost saving and around 14 ms on a single RTX 4090 GPU.
