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ARXIV:2605.31603 · GENERATIVE VIDEO · SUBMITTED 01 JUN · 20:21 UTC · FRESHNESS STALE
ARXIV:2605.31603GENERATIVE VIDEOSUBMITTED 01 JUN · 20:21 UTCFRESHNESS STALEJiazheng Xing · Hangjie Yuan · Lingling Cai · Xinyu Liu · Yujie Wei · Fei Du · +6 at arXiv
A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model.
Opportunity summary
Pain A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reasoning-driven generation…
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,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.…
Generative Video moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model.
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10.48550/arXiv.2605.31603A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model.
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 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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Proof status
unverified0 refs; 4 sources; 67% coverage.
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Dimensions overall score 7.0
PROBLEM
A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reason...
METHOD
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 propo...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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...
WHY NOW
Generative Video moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 34, "author": "Jiazheng Xing; Hangjie Yuan; Lingling Cai; Xinyu Liu; Yujie Wei; Fei Du; Hai Ci; Tao Feng; Jiasheng Tang; Weihua Chen; Fan Wang; Yong Liu"
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A training-efficient framework for high-fidelity video generation that progressively refines output using a high-capacity pretrained model.
Segment
Generative Video
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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2/3 checks · 67%
Build Passport
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status
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reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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passport absent
stale
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stale
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Gaps
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0 references, 4 sources, 67% evidence coverage.
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Classify regulatory flags before commercialization planning.
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People
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