UniVerse: A Unified Modulation Framework for Segmentation-Free,Disentangled Multi-Concept Personalization
Personalized visual understanding has advanced significantly, yet existing approaches struggle to localize and extract specific concepts when input images contain multiple objects. Many prior methods rely heavily on segmentation-based supervision or exhibit poor compositional generalization, limiting their ability to accurately disentangle and manipulate individual concepts. In this work, we propose UniVerse, a Unified Modulation Framework for segmentation-free, disentangled multi-concept personalization in diffusion transformers. Our method allows for composable and decomposable concept extraction, enabling fine-grained localization and representation of target objects without explicit s...
| Authors | Quynh Phung, Sandesh Ghimire, Minsi Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 2 June 2026 |
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