Text-to-motion models excel at efficient human motion generation, but
existing approaches lack fine-grained controllability over the generation
process. Consequently, modifying subtle postures within a motion or inserting
new actions at specific moments remains a challenge, limiting the applicability
of these methods in diverse scenarios. In light of these challenges, we
introduce CoMo, a Controllable Motion generation model, adept at accurately
generating and editing motions by leveraging the knowledge priors of large
language models (LLMs). Specifically, CoMo decomposes motions into discrete and
semantically meaningful pose codes, with each code encapsulating the semantics
of a body part, representing elementary information such as "left knee slightly
bent". Given textual inputs, CoMo autoregressively generates sequences of pose
codes, which are then decoded into 3D motions. Leveraging pose codes as
interpretable representations, an LLM can directly intervene in motion editing
by adjusting the pose codes according to editing instructions. Experiments
demonstrate that CoMo achieves competitive performance in motion generation
compared to state-of-the-art models while, in human studies, CoMo substantially
surpasses previous work in motion editing abilities.

CoMo 是一种可控运动生成模型，通过利用大型语言模型的先验知识，在准确生成和编辑动作方面表现出色。它将动作分解为离散且语义上有意义的姿势代码，每个代码都包含身体部位的语义信息，如 “左膝稍微弯曲”。通过调整姿势代码，CoMo 可以实现直接干预动作编辑。实验证明，与最先进的模型相比，CoMo 在运动生成方面具有竞争力，在人体研究中，CoMo 在动作编辑能力方面远远超过以前的工作。