Unimotion: Unifying 3D Human Motion Synthesis and Understanding

We introduce UniMotion, the first unified multi-task human motion model capable of both flexible motion control and frame-level motion understanding. While existing works control avatar motion with global text conditioning, or with fine-grained per frame scripts, none can do both at once. In addition, none of the existing works can output frame-level text paired with the generated poses. In contrast, UniMotion allows to control motion with global text, or local frame-level text, or both at once, providing more flexible control for users. Importantly, UniMotion is the first model which by design outputs local text paired with the generated poses, allowing users to know what motion happens and when, which is necessary for a wide range of applications. We show UniMotion opens up new applications: 1.) hierarchical control, allowing users to specify motion at different levels of detail, 2.) obtaining motion text descriptions for existing MoCap data or youtube videos 3.) allowing for editability, generating motion from text and editing the motion via text edits. Moreover, UniMotion attains state-of-the-art results for the frame-level text-to-motion task on the established HumanML3D dataset. The pre-trained model and code are available available on our project page at https://coral79.github.io/uni-motion/.

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Publication Details

Published
2025-03-25
DOI
https://doi.org/10.1109/3dv66043.2025.00027
Citations
3
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
3.83

Funders

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article

Unimotion: Unifying 3D Human Motion Synthesis and Understanding

Andreas Geiger, Julian Chibane, N. Pearl, Yannan He et al.
3 citations
Human Pose and Action Recognition
3.83
article

Unimotion: Unifying 3D Human Motion Synthesis and Understanding

Andreas Geiger, Julian Chibane, N. Pearl, Yannan He, Cun Li, Gerard Pons‐Moll
article en
3 citations

Abstract

We introduce UniMotion, the first unified multi-task human motion model capable of both flexible motion control and frame-level motion understanding. While existing works control avatar motion with global text conditioning, or with fine-grained per frame scripts, none can do both at once. In addition, none of the existing works can output frame-level text paired with the generated poses. In contrast, UniMotion allows to control motion with global text, or local frame-level text, or both at once, providing more flexible control for users. Importantly, UniMotion is the first model which by design outputs local text paired with the generated poses, allowing users to know what motion happens and when, which is necessary for a wide range of applications. We show UniMotion opens up new applications: 1.) hierarchical control, allowing users to specify motion at different levels of detail, 2.) obtaining motion text descriptions for existing MoCap data or youtube videos 3.) allowing for editability, generating motion from text and editing the motion via text edits. Moreover, UniMotion attains state-of-the-art results for the frame-level text-to-motion task on the established HumanML3D dataset. The pre-trained model and code are available available on our project page at https://coral79.github.io/uni-motion/.

TH Bingen University of Applied Sciences (DE)
Carl-Zeiss-Stiftung
Openalex Percentile: Top 6%
Human Pose and Action Recognition
3.83
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