Efficient Text-driven Motion Generation via Latent Consistency Training

arXiv:2405.02791v1 Announce Type: new
Abstract: Motion diffusion models have recently proven successful for text-driven human motion generation. Despite their excellent generation performance, they are challenging to infer in real time due to the multi-step sampling mechanism that involves tens or hundreds of repeat function evaluation iterations. To this end, we investigate a motion latent consistency Training (MLCT) for motion generation to alleviate the computation and time consumption during iteration inference. It applies diffusion pipelines to low-dimensional motion latent spaces to …

abstract arxiv cs.ai cs.cv diffusion diffusion models evaluation function human performance repeat sampling text training type via