Paper Detail
Jiazheng Xing, Hangjie Yuan, Lingling Cai, Xinyu Liu, Yujie Wei, Fei Du, Hai Ci, Tao Feng, Jiasheng Tang, Weihua Chen, Fan Wang, Yong Liu
Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified training loop is computationally prohibitive, limiting achievable visual quality. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reasoning-driven generation capabilities while significantly enhancing visual fidelity. Lumos-Nexus adopts a two-stage design: 1) During training, only a lightweight generator is aligned with the understanding block to learn to take in reasoning-driven semantic control. 2) During inference, we introduce Unified Progressive Frequency Bridging (UPFB) to progressively hand off generation to a high-capacity pretrained generator in the shared latent space, enabling coarse-to-fine refinement and producing high-fidelity videos without compromising reasoning quality. To fill the gap in reasoning-driven video generation benchmarks, we introduce VR-Bench, which assesses a model's capability to translate inferred intent into coherent and semantically aligned video content. Extensive experiments demonstrate that Lumos-Nexus achieves substantial gains in visual realism and temporal coherence on VBench, while exhibiting strong reasoning-based generative performance on VR-Bench. Code and models are available at https://jiazheng-xing.github.io/nexus-lumos-home/.
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@article{xing2026lumos,
title = {Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models},
author = {Jiazheng Xing and Hangjie Yuan and Lingling Cai and Xinyu Liu and Yujie Wei and Fei Du and Hai Ci and Tao Feng and Jiasheng Tang and Weihua Chen and Fan Wang and Yong Liu},
year = {2026},
abstract = {Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified training loop is computationally prohibitive, limiting achievable visual quality. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reasoning-driven generation capabilities while significantly enhancing visual fidelity. Lumos-Nexus adopts},
url = {https://arxiv.org/abs/2605.31603},
keywords = {cs.CV, cs.AI},
eprint = {2605.31603},
archiveprefix = {arXiv},
}
{}