Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication is inherently multimodal, where visual instructions such as sketches efficiently convey spatial and structural intent. To address this gap, we introduce VIBE, the Visual Instruction Benchmark for Image Editing with a three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning. Across these levels, we curate high-quality and diverse test cases that reflect progressively increasing complexity in visual instruction following. We further propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment. Through a comprehensive evaluation of 17 representative open-source and proprietary image editing models, we find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models. However, performance degrades markedly with increasing task difficulty even for the strongest systems, highlighting promising directions for future research.

翻译：近期生成模型在图像编辑领域取得了显著进展。然而，现有系统和基准仍主要基于文本引导。相比之下，人类交流本质上是多模态的，其中草图等视觉指令能有效传达空间与结构意图。为填补这一空白，我们提出了VIBE（视觉指令图像编辑基准），其采用包含指示性定位、形态操控与因果推理的三层交互层次结构。基于这三个层级，我们构建了高质量且多样化的测试案例，以反映视觉指令遵循任务中渐进增加的复杂度。我们进一步提出一个鲁棒的LMM-as-a-judge评估框架，结合任务特异性指标，以实现可扩展的细粒度评估。通过对17个代表性开源与专有图像编辑模型的系统性评测，我们发现专有模型展现出早期视觉指令遵循能力，且持续优于开源模型。然而，随着任务难度增加，即使是最强的系统其性能也会显著下降，这为未来研究指明了具有潜力的方向。