| title | UniSpace: Unified Visual Representation and Scalable Multimodal Modeling |
|---|---|
| authors | Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan |
| date | 2026-08-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.08676 |
| affiliation | LongCat |
| upvotes | 12 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation. However, their final tokens discard fine-grained visual details, leading to poor pixel reconstruction and limiting their use in reconstruction-sensitive tasks such as image generation and editing. In this work, we ask whether understanding, generation, and editing can be modeled in a single visual representation space built from a pretrained semantic ViT. We show that the frozen Transformer blocks of a semantic ViT are not intrinsically unable to preserve visual details. Instead, the original patch parameterization drives the representation toward semantic abstraction, making fine-grained information difficult to recover from the final tokens. Based on this observation, we introduce Patch Reparameterization, which preserves the original semantic pathway while adding a reconstruction-aware patch embedding that provides fine-grained visual information to the same frozen ViT blocks. The resulting unified representation preserves multimodal understanding while enabling high-fidelity image reconstruction and a favorable reconstruction--generation trade-off. We further scale this representation into UniSpace, an 8B Mixture-of-Transformer-Experts model that performs understanding, generation, and editing in the same visual space without a separate VAE pathway. System-level evaluations demonstrate practical text-to-image generation and instruction-based image editing, showing that a reparameterized pretrained ViT can serve as a unified visual interface for scalable multimodal modeling. |
cite
@misc{yan2026unispace,
title={UniSpace: Unified Visual Representation and Scalable Multimodal Modeling},
author={Jinbo Yan and Limeng Qiao and Jie Qin and Junyan He and Feize Wu and Guanglu Wan},
year={2026},
eprint={2608.08676},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.08676}
}