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UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
Open accessAbstract
We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles: preserving consistency between inputs and outputs while capturing visual variations. Inspired by recent video generation models that effectively balance consistency and variation across frames, we propose a unifying approach that treats image-level tasks as discontinuous video generation. Specifically, we treat varying numbers of input and output images as frames, enabling seamless support for tasks such as image generation, editing, customization, composition, etc. Although designed for image-level tasks, we leverage videos as a scalable source for universal supervision. UniReal learns world dynamics from large-scale videos, demonstrating advanced capability in handling shadows, reflections, pose variation, and object interaction, while also exhibiting emergent capability for novel applications.
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Chow, W., Li, L., Sun, X., Kong, L., Li, Z., Xu, Q., Song, H., Tian, Y., Wang, X., Bai, J., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.10859 · Cited by 4
Next-Frame Decoding for Ultra-low-Bitrate Image Compression with Video Diffusion Priors
Chen, Y., Zhou, C., Li, J., Ling, X., He, B., Dai, J., Song, L., & Lu, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37422-6_15 · Cited by 3
ReMoT: Reinforcement Learning with Motion Contrast TripletsPreprint
Wan, C., Guo, Z., Li, J., Dong, S., Bai, Y., Peng, L., Ma, Z., & Gong, Y. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2603.00461 · Cited by 3
Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision
Pu, Y., Zhuo, L., Zhu, K., Xie, L., Zhang, W., Chen, X., Gao, P., Qiao, Y., Dong, C., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37041-9_22 · Cited by 13
Self-citationUniversalBooth: Model-Agnostic Personalized Text-To-Image Generation
Liu, S., Yu, R., & Wang, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01702 · Cited by 2
CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image GenerationPreprint
Song, Y., Lu, Y., Sun, H., Yao, H., Liu, F., Sun, Y., Feng, H., Zhou, H., & Wang, J. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.22150 · Cited by 2
TIDE: Task-Isolated Diffusion for Unified Video Editing and GenerationPreprint
Liu, Q., Yue, G., Yin, M., Zhang, L., Wu, Y., Wang, Y., Wang, Y., Yao, C., Chen, J., & Ma, L. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.08260 · Cited by 2
Self-citationRealgeneral: Unifying Visual Generation Via Temporal in-Context Learning With Video Models
Lin, Y., Huang, M., Zhuang, S., & Mao, Z. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01391 · Cited by 1
HOComp: Interaction-Aware Human-Object Composition
Liang, D., Jia, J., Liu, Y., & Lau, R. W. H. (2025) · neural information processing systems · doi:10.52202/085713-3263 · Cited by 1
Xu, Y., Tang, F., Wu, Y., Gao, L., Deussen, O., Yan, H., Li, J., Cao, J., & Lee, T.-Y. (2025) · SIGGRAPH Asia Conference Papers · doi:10.1145/3757377.3763820 · Cited by 1
OmniColor: A Unified Framework for Multi-modal Lineart Colorization
Zhang, X., Du, H., Wei, X., & Li, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36987-1_21 · Cited by 1
Edit Where You Mean: Region-Aware Adapter Injection for Mask-Free Local Image EditingPreprint
Cai, H., Wang, X., Bai, Y., Chen, H., Zhou, T., Wang, R., Zhu, W., Chen, Y., Tang, X., Hu, Y., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.23763 · Cited by 1
InsertFuse: A Unified Framework for Multi-Category Reference-Guided Image InsertionPreprint
Li, G., Wei, Q., Zheng, H., Zheng, Y., Zhang, C., Yang, J., Ding, Y., Tai, Y., Luo, S., & Liu, X. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.06490 · Cited by 1
Kwon, P., & Chen, C. (2025) · arXiv · doi:10.48550/arxiv.2512.01686 · Cited by 1
“Several works [3, 9, 28] have also used video models for controllable image generation, demonstrating their capabilities to unify diverse image tasks.”
FullDiT: Multi-Task Video Generative Foundation Model with Full Attention
Ju, X., Ye, W., Liu, Q., Wang, Q., Wang, X., Wan, P., Zhang, D., Gai, K., & Xu, Q. (2025) · arXiv · doi:10.48550/arxiv.2503.19907
Self-citationA survey: advances in multi-modal visual understanding and generation
Xi, D., Zhou, Z., Wang, Y., Wang, J., & Zhang, C. (2026) · Vicinagearth. · doi:10.1007/s44336-025-00032-x · Cited by 1
Self-citationA Parallel U-Net Concept for Image Recontextualization
Skorupa, A., Balicki, J., Karanowski, K., Dróżdż, T., Hałas, T., Lyu, H., Parellada, O. C., Tomczak, J. M., & Zięba, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0071-9_29
Adaptive spatio-temporal reasoning for physically-consistent region-aware image editing
Li, G., & Ye, M. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02568-4
PhysEdit: Physically Consistent Image Editing via Causal Enforcement
Wan, S., Chen, J., Li, Y., Pan, Y., Yao, T., & Mei, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37023-5_24
Self-citationVGEdit: Unlocking Video Generation Priors for Reasoning-Informed Image Editing
Lu, H., Fang, G., Ma, X., & Wang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37092-1_21
Xia, T., Ning, Z., Zhao, Z., & Wang, M. (2026) · doi:10.1109/cvprf71909.2026.00370
Quantifying the Gap between Understanding and Generation within Unified Multimodal Models
Wang, C., Chen, Y., Hu, Z., Chen, D., Chen, W., Wiegreffe, S., & Zhou, T. (2026) · doi:10.1109/cvprf71909.2026.00570
Metadata
- Authors
- Chen, X., Zhang, Z., Zhang, H., Zhou, Y., Kim, S. Y., Liu, Q., Li, Y., Zhang, J., Zhao, N., Wang, Y., Ding, H., Lin, Z., & Zhao, H.
- Affiliations
- University of Hong Kong (HK)
- Adobe Systems (United States) (US)
- ORCID
- Venue
- 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2025-06-10
- Pages
- 12501-12511
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4413157087
- Cited by
- 23
- References
- 32
- Citations by year
- 2026: 15 · 2025: 8
- FWCI
- 17.38
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- image generation, image editing, image composition, video supervision, pose variation, object interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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