WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing
Kaihang Pan, Weile Chen, Haiyi Qiu, Qifan Yu, Wendong Bu, Zehan Wang, Yun Zhu, Juncheng Li, Siliang Tang
Abstract
Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation of cognition- and creativity-informed image editing, featuring deep task depth and broad knowledge breadth. Drawing an analogy to human cognitive creation, WiseEdit decomposes image editing into three cascaded steps, i.e., Awareness, Interpretation, and Imagination, each corresponding to a task that poses a challenge for models to complete at the specific step. It also encompasses complex tasks, where none of the three steps can be finished easily. Furthermore, WiseEdit incorporates three fundamental types of knowledge: Declarative, Procedural, and Metacognitive knowledge. Ultimately, WiseEdit comprises 1,220 test cases, objectively revealing the limitations of SoTA image editing models in knowledge-based cognitive reasoning and creative composition capabilities. The benchmark, evaluation code, and the generated images of each model will be made publicly available soon. Project Page: https://qnancy.github.io/wiseedit_project_page/.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8d7044ae-634d-47ff-ad82-0a30b1055bf7Cited by top-tier papers2
- Unified Personalized Understanding, Generating and EditingYu Zhong, Tianwei Lin, Ruike Zhu, Yuqian Yuan et al.CVPR 2026 · 7 citations
- Benchmarking and Improving Fine-Grained Text-to-Image Alignment via Paired Reinforcement LearningKaihang Pan, Wendong Bu, Yuruo Wu, Kai Shen et al.ICML 2026
Builds on18
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Scalable Diffusion Models with TransformersWilliam Peebles, Saining XieICCV 2023 · 5,568 citations
- Scaling Rectified Flow Transformers for High-Resolution Image SynthesisPatrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari et al.ICML 2024 · 3,620 citations
- OmniGen2: Towards Instruction-Aligned Multimodal GenerationChenyuan Wu, Jiahao Wang, Pengfei Zheng, Ruiran Yan et al.CVPR 2026 · 231 citations
- WISE: World Knowledge-Informed Semantic Evaluation for Text-to-Image GenerationYuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin et al.ICML 2026 · 195 citations
Related papers
- Towards Meta-Cognitive Knowledge Editing for Multimodal LLMsZhaoyu Fan, Kaihang Pan, Mingze Zhou, Bosheng Qin et al.WWW 2026
- MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual KnowledgeYuntao Du, Kailin Jiang, Zhi Gao, Chenrui Shi et al.ICLR 2025
- Creation-Mmbench: Assessing Context-Aware Creative Intelligence in MllmsXinyu Fang, Zhijian Chen, Kai Lan, Lixin Ma et al.ICCV 2025 · 23 citations
- CompBench: Benchmarking Complex Instruction-guided Image EditingBohan Jia, Wenxuan Huang, Yuntian Tang, Junbo Qiao et al.CVPR 2026 · 17 citations
- I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing ModelsJuntong Wang, Jiarui Wang, Huiyu Duan, Jiaxiang Kang et al.CVPR 2026 · 9 citations
