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Unimotion: Unifying 3D Human Motion Synthesis and Understanding
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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/.
Forward citations
Cited by 6 Pith papers
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MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation
Distilling frozen Motion-JEPA features into a compact 32-D latent whose geometry is coupled to the decoder lets a standard non-autoregressive flow-matching DiT reach state-of-the-art text-to-motion quality on HumanML3...
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Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression
A masked-autoregressive diffusion model trained on a compact essential-feature latent space claims state-of-the-art text-to-motion generation under a new essential-dimension evaluation protocol.
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MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation
A semantically aligned latent space plus multi-token cross-attention conditioning sets a new state of the art in text-to-human-motion generation on HumanML3D.
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MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm
MotionLab unifies text-based and trajectory-based motion generation with text-based editing, trajectory-based editing, motion in-betweening, and style transfer in one flow-based transformer.
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CASIM: Composite Aware Semantic Injection for Text to Motion Generation
CASIM replaces fixed-length text embeddings with token-level cross-attention in text-to-motion models, improving alignment and quality for both diffusion and autoregressive generators.
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SCENIC: Scene-aware Semantic Navigation with Instruction-guided Control
A diffusion model generates human motion that simultaneously follows text instructions and adapts to complex 3D terrain, using goal-centric canonicalization and an ego-centric distance field.
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