ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies

• URL: http://arxiv.org/abs/2506.12830v1
• Date: Sun, 15 Jun 2025 12:22:55 GMT
• Title: ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies
• Authors: Chenglin Wang, Yucheng Zhou, Qianning Wang, Zhe Wang, Kai Zhang,
• Abstract summary: Real-world scenarios often involve complex, multi-step instructions, particularly chain'' instructions where operations are interdependent.<n>Current models struggle with these intricate directives, and existing benchmarks inadequately evaluate such capabilities.<n>We introduce ComplexBench-Edit, a novel benchmark designed to systematically assess model performance on complex, multi-instruction, and chain-dependent image editing tasks.
• Score: 13.525744033075785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-driven image editing has achieved remarkable success in following single instructions. However, real-world scenarios often involve complex, multi-step instructions, particularly ``chain'' instructions where operations are interdependent. Current models struggle with these intricate directives, and existing benchmarks inadequately evaluate such capabilities. Specifically, they often overlook multi-instruction and chain-instruction complexities, and common consistency metrics are flawed. To address this, we introduce ComplexBench-Edit, a novel benchmark designed to systematically assess model performance on complex, multi-instruction, and chain-dependent image editing tasks. ComplexBench-Edit also features a new vision consistency evaluation method that accurately assesses non-modified regions by excluding edited areas. Furthermore, we propose a simple yet powerful Chain-of-Thought (CoT)-based approach that significantly enhances the ability of existing models to follow complex instructions. Our extensive experiments demonstrate ComplexBench-Edit's efficacy in differentiating model capabilities and highlight the superior performance of our CoT-based method in handling complex edits. The data and code are released at https://github.com/llllly26/ComplexBench-Edit.

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