  We study a challenging task: text-to-motion synthesis, aiming to generate
motions that align with textual descriptions and exhibit coordinated movements.
Currently, the part-based methods introduce part partition into the motion
synthesis process to achieve finer-grained generation. However, these methods
encounter challenges such as the lack of coordination between different part
motions and difficulties for networks to understand part concepts. Moreover,
introducing finer-grained part concepts poses computational complexity
challenges. In this paper, we propose Part-Coordinating Text-to-Motion
Synthesis (ParCo), endowed with enhanced capabilities for understanding part
motions and communication among different part motion generators, ensuring a
coordinated and fined-grained motion synthesis. Specifically, we discretize
whole-body motion into multiple part motions to establish the prior concept of
different parts. Afterward, we employ multiple lightweight generators designed
to synthesize different part motions and coordinate them through our part
coordination module. Our approach demonstrates superior performance on common
benchmarks with economic computations, including HumanML3D and KIT-ML,
providing substantial evidence of its effectiveness. Code is available at
https://github.com/qrzou/ParCo .
