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New LynnReal-Omni framework enables multimodal video generation for agents

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced LynnReal-Omni, a novel multimodal video generation framework designed for agentic visual workflows. This system utilizes a 32B shared multimodal diffusion transformer to unify various video generation tasks, including text-to-video, image-conditioned generation, and long-video generation, accepting diverse visual inputs like 3D renders and game recordings. A faster version, LynnReal-Omni-Flash, has also been developed for real-time rendering, achieving 22-frame 540p video generation in under a second on an NVIDIA H100. The framework is supported by a comprehensive data pipeline and a new evaluation design, MSAVP, to assess video generation quality across multiple dimensions. AI

IMPACT Enables more controllable and higher-fidelity video generation for AI agents, potentially accelerating visual content creation.

RANK_REASON Research paper detailing a new multimodal video generation framework. [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 →

New LynnReal-Omni framework enables multimodal video generation for agents

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Research paper detailing a new multimodal video generation framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaofeng Mao, Peijia Lin, Shaohao Rui, Yibo Zhang, Haibin Wan, Weijie Ma ·

    LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows

    arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon scenes drift in appearance, interactions, and temporal coherence. Agentic visua…

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