意外之笔：矢量素描中的渐进式语义错觉

摘要

传统视觉错觉通常依赖于多视角一致性等空间操控技术。在本研究中，我们提出了渐进式语义错觉——一种创新的矢量草图绘制任务，通过逐笔添加使单幅草图实现剧烈的语义转换。我们开发了"惊鸿一笔"生成框架，通过优化矢量笔触使其在不同绘制阶段满足不同的语义解读。该任务的核心挑战在于"双重约束"：初始前缀笔触既要构成连贯物体（如鸭子），又需作为添加增量笔触后第二概念（如绵羊）的结构基础。为此，我们提出基于双分支分数蒸馏采样机制的序列感知联合优化框架。与固定初始状态的序列方法不同，我们的方法能动态调整前缀笔触，探索适用于两个目标的"共同结构子空间"。此外，我们创新性地提出了叠层损失函数，通过强制空间互补性确保结构整合而非简单遮挡。大量实验表明，本方法在识别度和错觉强度上显著优于现有基线，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/

English

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/

意外之笔：矢量素描中的渐进式语义错觉

Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching

摘要

Support