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

Paper recorded by Signals 4 on 2026-09-14 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.CV · 计算机视觉 · first seen 2026-09-15

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 visual creation provides explicit references, editable 3D scenes, or executable game states for stable control, but does not by itself guarantee high object or character fid

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#45 most recent of 237 cs.CV papers we have recorded · ↑ newer: Multimodal Cultural Heritage Architectural Style Classification for Re · ↓ older: TRACE: Two-Stage Detector-Response Estimation With Angular Cosine Expa
Cite this page: LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows: the #45 most recent of 237 cs.CV papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/lynnreal-omni-native-multi-modal-video-generation-for-agentic-visual-workflows.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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