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Text-driven motion generation has achieved substantial progress with theemergence of diffusion models. However, existing methods still struggle togenerate complex motion sequences that correspond to fine-grained descriptions,depicting detailed and accurate spatio-temporal actions. This lack of finecontrollability limits the usage of motion generation to a larger audience. Totackle these challenges, we present FineMoGen, a diffusion-based motiongeneration and editing framework that can synthesize fine-grained motions, withspatial-temporal composition to the user instructions. Specifically, FineMoGenbuilds upon diffusion model with a novel transformer architecture dubbedSpatio-Temporal Mixture Attention (SAMI). SAMI optimizes the generation of theglobal attention template from two perspectives: 1) explicitly modeling theconstraints of spatio-temporal composition; and 2) utilizing sparsely-activatedmixture-of-experts to adaptively extract fine-grained features. To facilitate alarge-scale study on this new fine-grained motion generation task, wecontribute the HuMMan-MoGen dataset, which consists of 2,968 videos and 102,336fine-grained spatio-temporal descriptions. Extensive experiments validate thatFineMoGen exhibits superior motion generation quality over state-of-the-artmethods. Notably, FineMoGen further enables zero-shot motion editingcapabilities with the aid of modern large language models (LLM), whichfaithfully manipulates motion sequences with fine-grained instructions. ProjectPage: https://mingyuan-zhang.github.io/projects/FineMoGen.html

FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing