Overview
Field: CV/AI Authors: Jiazheng Xing, Hangjie Yuan, Lingling Cai, Xinyu Liu, et al. Published: 2026-05-29 arXiv: 2605.31603 PDF: 2605.31603.pdf
Abstract
Connector-based unified video models have shown strong capability in instruction-guided video synthesis, but integrating large, high-fidelity generators into a unified training loop is computationally prohibitive, limiting visual quality.
This paper proposes Lumos-Nexus, a training-efficient unified video generation framework that significantly enhances visual fidelity while cultivating reasoning-driven generation capability. It adopts a two-stage design:
1. During training, only a lightweight generator is aligned with the understanding module. 2. At inference, a Unified Progressive Frequency Bridging (UPFB) mechanism is introduced, which progressively hands generation over to a high-capacity pretrained generator in a shared latent space, enabling coarse-to-fine refinement.
To fill the gap in evaluating reasoning-driven video generation, the authors introduce VR-Bench, a dedicated benchmark.
Results
Experiments demonstrate that Lumos-Nexus achieves significant improvements in visual realism and temporal consistency on VBench, while exhibiting strong reasoning-driven generation performance on VR-Bench.
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