Visual illusions traditionally rely on spatial manipulations such as multi-view consistency. In this work, we introduce Progressive Semantic Illusions, a novel vector sketching task where a single sketch undergoes a dramatic semantic transformation through the sequential addition of strokes. We present Stroke of Surprise, a generative framework that optimizes vector strokes to satisfy distinct semantic interpretations at different drawing stages. The core challenge lies in the "dual-constraint": initial prefix strokes must form a coherent object (e.g., a duck) while simultaneously serving as the structural foundation for a second concept (e.g., a sheep) upon adding delta strokes. To address this, we propose a sequence-aware joint optimization framework driven by a dual-branch Score Distillation Sampling (SDS) mechanism. Unlike sequential approaches that freeze the initial state, our method dynamically adjusts prefix strokes to discover a "common structural subspace" valid for both targets. Furthermore, we introduce a novel Overlay Loss that enforces spatial complementarity, ensuring structural integration rather than occlusion. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines in recognizability and illusion strength, successfully expanding visual anagrams from the spatial to the temporal dimension. Project page: https://stroke-of-surprise.github.io/

翻译：传统视觉错觉通常依赖于多视角一致性等空间操纵。本研究提出渐进式语义错觉，这是一种新颖的矢量素描任务：通过笔画的顺序叠加，单幅素描会发生剧烈的语义转变。我们提出"意外之笔"生成框架，通过优化矢量笔画使素描在不同绘制阶段满足截然不同的语义解释。核心挑战在于"双重约束"：初始前缀笔画必须构成连贯物体（如鸭子），同时还需作为添加增量笔画后第二概念（如绵羊）的结构基础。为此，我们提出由双分支分数蒸馏采样机制驱动的序列感知联合优化框架。与冻结初始状态的顺序方法不同，我们的方法动态调整前缀笔画以发现适用于两个目标的"共同结构子空间"。此外，我们提出新颖的重叠损失函数来增强空间互补性，确保结构整合而非遮挡。大量实验表明，本方法在可识别性与错觉强度上显著优于现有基线方法，成功将视觉字谜从空间维度拓展至时间维度。项目页面：https://stroke-of-surprise.github.io/