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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.05781 |
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| _version_ | 1866911655145766912 |
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| author | Liu, Zeyu Ni, Zanlin Yue, Yang Da, Cheng Yang, Huan Zhang, Di Gai, Kun Huang, Gao |
| author_facet | Liu, Zeyu Ni, Zanlin Yue, Yang Da, Cheng Yang, Huan Zhang, Di Gai, Kun Huang, Gao |
| contents | Unified multimodal models are envisioned to bridge the gap between understanding and generation. Yet, to achieve competitive performance, state-of-the-art models adopt largely decoupled understanding and generation components. This design, while effective for individual tasks, weakens the connection required for mutual enhancement, leaving the potential synergy empirically uncertain. We propose to explicitly restore this synergy by introducing Understanding-Oriented Post-Training (UNO), a lightweight framework that treats understanding not only as a distinct task, but also a direct supervisory signal to steer generative representations. By incorporating objectives that encode semantic abstraction (captioning) and structural details (visual regression), we enable effective gradient flow from understanding to generation. Extensive experiments on image generation and editing demonstrate that understanding can serve as an effective catalyst for generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05781 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Steering Visual Generation in Unified Multimodal Models with Understanding Supervision Liu, Zeyu Ni, Zanlin Yue, Yang Da, Cheng Yang, Huan Zhang, Di Gai, Kun Huang, Gao Computer Vision and Pattern Recognition Artificial Intelligence Unified multimodal models are envisioned to bridge the gap between understanding and generation. Yet, to achieve competitive performance, state-of-the-art models adopt largely decoupled understanding and generation components. This design, while effective for individual tasks, weakens the connection required for mutual enhancement, leaving the potential synergy empirically uncertain. We propose to explicitly restore this synergy by introducing Understanding-Oriented Post-Training (UNO), a lightweight framework that treats understanding not only as a distinct task, but also a direct supervisory signal to steer generative representations. By incorporating objectives that encode semantic abstraction (captioning) and structural details (visual regression), we enable effective gradient flow from understanding to generation. Extensive experiments on image generation and editing demonstrate that understanding can serve as an effective catalyst for generation. |
| title | Steering Visual Generation in Unified Multimodal Models with Understanding Supervision |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05781 |