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OneLA framework learns unified latent actions for diverse world models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OneLA, a novel framework designed to learn a unified control space for world models across diverse domains. This approach utilizes latent actions derived directly from videos, bypassing the need for explicit action supervision. OneLA employs a shared orthogonal dictionary and a common predictive objective to learn these latent actions, reducing action cycle-transfer rotation error by 24.8% and improving human preference scores for real-scene generation by 20.5%. The framework aims to enable general-purpose world models that can learn from various videos and be controlled through this learned latent action space. AI

IMPACT Enables more general-purpose world models by providing a unified latent action space for diverse video data.

RANK_REASON Research paper detailing a new framework for learning latent actions in world models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

OneLA framework learns unified latent actions for diverse world models

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Research paper detailing a new framework for learning latent actions in world models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ziyu Zhu, Zhaoxu Sun, Fan Jiang, Chiyu Wang, Junfeng Ni, Mu Xu ·

    OneLA: Learning Many Worlds from Latent Actions

    arXiv:2610.20160v1 Announce Type: new Abstract: Learning world models across diverse domains calls for a unified control space, yet ac- tion interfaces vary across domains and action annotations are often scarce. Latent ac- tions learned directly from videos offer a promising way…

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