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

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

22 Dec 2023NeurIPS 2023 11arXiv:2312.15004archive 2025-07-28

Mingyuan Zhang, Huirong Li, Zhongang Cai, Jiawei Ren, Lei Yang, Ziwei Liu

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

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Tasks

Mixture-of-ExpertsMotion GenerationMotion Synthesis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Motion Synthesis HumanML3D FineMoGen Diversity 9.263 #24 of 37 Archive leaderboard report
Motion Synthesis HumanML3D FineMoGen FID 0.151 #24 of 37 Archive leaderboard report
Motion Synthesis HumanML3D FineMoGen Multimodality 2.696 #24 of 37 Archive leaderboard report
Motion Synthesis HumanML3D FineMoGen R Precision Top3 0.784 #24 of 37 Archive leaderboard report
Motion Synthesis KIT Motion-Language FineMoGen Diversity 10.85 #6 of 31 Archive leaderboard report
Motion Synthesis KIT Motion-Language FineMoGen FID 0.178 #6 of 31 Archive leaderboard report
Motion Synthesis KIT Motion-Language FineMoGen Multimodality 1.877 #6 of 31 Archive leaderboard report
Motion Synthesis KIT Motion-Language FineMoGen R Precision Top3 0.772 #6 of 31 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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