  Text-driven motion generation has achieved substantial progress with the
emergence of diffusion models. However, existing methods still struggle to
generate complex motion sequences that correspond to fine-grained descriptions,
depicting detailed and accurate spatio-temporal actions. This lack of fine
controllability limits the usage of motion generation to a larger audience. To
tackle these challenges, we present FineMoGen, a diffusion-based motion
generation and editing framework that can synthesize fine-grained motions, with
spatial-temporal composition to the user instructions. Specifically, FineMoGen
builds upon diffusion model with a novel transformer architecture dubbed
Spatio-Temporal Mixture Attention (SAMI). SAMI optimizes the generation of the
global attention template from two perspectives: 1) explicitly modeling the
constraints of spatio-temporal composition; and 2) utilizing sparsely-activated
mixture-of-experts to adaptively extract fine-grained features. To facilitate a
large-scale study on this new fine-grained motion generation task, we
contribute the HuMMan-MoGen dataset, which consists of 2,968 videos and 102,336
fine-grained spatio-temporal descriptions. Extensive experiments validate that
FineMoGen exhibits superior motion generation quality over state-of-the-art
methods. Notably, FineMoGen further enables zero-shot motion editing
capabilities with the aid of modern large language models (LLM), which
faithfully manipulates motion sequences with fine-grained instructions. Project
Page: https://mingyuan-zhang.github.io/projects/FineMoGen.html
