CompBench: Benchmarking Complex Instruction-guided Image Editing

• URL: http://arxiv.org/abs/2505.12200v2
• Date: Tue, 20 May 2025 11:17:17 GMT
• Title: CompBench: Benchmarking Complex Instruction-guided Image Editing
• Authors: Bohan Jia, Wenxuan Huang, Yuntian Tang, Junbo Qiao, Jincheng Liao, Shaosheng Cao, Fei Zhao, Zhaopeng Feng, Zhouhong Gu, Zhenfei Yin, Lei Bai, Wanli Ouyang, Lin Chen, Fei Zhao, Zihan Wang, Yuan Xie, Shaohui Lin,
• Abstract summary: CompBench is a large-scale benchmark for complex instruction-guided image editing.<n>We propose an MLLM-human collaborative framework with tailored task pipelines.<n>We propose an instruction decoupling strategy that disentangles editing intents into four key dimensions.
• Score: 63.347846732450364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack comprehensive, fine-grained instructions. To bridge this gap, we introduce, a large-scale benchmark specifically designed for complex instruction-guided image editing. CompBench features challenging editing scenarios that incorporate fine-grained instruction following, spatial and contextual reasoning, thereby enabling comprehensive evaluation of image editing models' precise manipulation capabilities. To construct CompBench, We propose an MLLM-human collaborative framework with tailored task pipelines. Furthermore, we propose an instruction decoupling strategy that disentangles editing intents into four key dimensions: location, appearance, dynamics, and objects, ensuring closer alignment between instructions and complex editing requirements. Extensive evaluations reveal that CompBench exposes fundamental limitations of current image editing models and provides critical insights for the development of next-generation instruction-guided image editing systems. The dataset, code, and models are available in https://comp-bench.github.io/.

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