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UniMate model generates diverse skeleton animations from text prompts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed UniMate, a novel foundation model capable of generating articulated motion for diverse skeletons based on text prompts and rigged 3D assets. This model utilizes a topology-aware diffusion transformer that incorporates skeletal structure through graph-aware attention, spectral rotary position embeddings, and a global topological conditioner. UniMate was trained on the newly curated UniML3D dataset, comprising over 13,000 motion sequences across various creature types and objects, and demonstrates superior generalization and efficiency compared to existing methods. AI

IMPACT This model could significantly streamline 3D animation workflows by enabling text-driven motion generation for arbitrary skeletons.

RANK_REASON The cluster contains an academic paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

UniMate model generates diverse skeleton animations from text prompts

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The cluster contains an academic paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Linzhan Mou, Jiahui Lei, Zhiyang Dou, Chenyue Cai, Chaoyue Song, Adam Finkelstein, Szymon Rusinkiewicz ·

    UniMate: One Unified Model to Animate Diverse Skeletons

    arXiv:2609.05415v1 Announce Type: cross Abstract: Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion to drive them remains a bottleneck. Existing learned animators are topology-constrained: they rely on category-specific…

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