  Human motion generation, a cornerstone technique in animation and video
production, has widespread applications in various tasks like text-to-motion
and music-to-dance. Previous works focus on developing specialist models
tailored for each task without scalability. In this work, we present Large
Motion Model (LMM), a motion-centric, multi-modal framework that unifies
mainstream motion generation tasks into a generalist model. A unified motion
model is appealing since it can leverage a wide range of motion data to achieve
broad generalization beyond a single task. However, it is also challenging due
to the heterogeneous nature of substantially different motion data and tasks.
LMM tackles these challenges from three principled aspects: 1) Data: We
consolidate datasets with different modalities, formats and tasks into a
comprehensive yet unified motion generation dataset, MotionVerse, comprising 10
tasks, 16 datasets, a total of 320k sequences, and 100 million frames. 2)
Architecture: We design an articulated attention mechanism ArtAttention that
incorporates body part-aware modeling into Diffusion Transformer backbone. 3)
Pre-Training: We propose a novel pre-training strategy for LMM, which employs
variable frame rates and masking forms, to better exploit knowledge from
diverse training data. Extensive experiments demonstrate that our generalist
LMM achieves competitive performance across various standard motion generation
tasks over state-of-the-art specialist models. Notably, LMM exhibits strong
generalization capabilities and emerging properties across many unseen tasks.
Additionally, our ablation studies reveal valuable insights about training and
scaling up large motion models for future research.
