驚奇之筆：向量素描中的漸進語義幻覺

摘要

傳統視覺錯覺主要依賴多視角一致性等空間操控技術。本研究提出「漸進語義錯覺」——一種新穎的向量素描任務，通過逐筆添加筆劃實現單幅素描的語義劇變。我們開發了Stroke of Surprise生成框架，通過優化向量筆劃使同一素描在不同繪製階段呈現截然不同的語義解讀。核心挑戰在於「雙重約束」：初始前綴筆劃既要構成連貫物體（如鴨子），又需作為添加增量筆劃後第二概念（如綿羊）的結構基礎。為此，我們提出由雙分支分數蒸餾採樣機制驅動的序列感知聯合優化框架。有別於凍結初始狀態的序列方法，我們的技術能動態調整前綴筆劃，探索對兩個目標均有效的「共通結構子空間」。此外，我們創新性地引入疊加損失函數來強化空間互補性，確保結構融合而非簡單遮擋。大量實驗表明，本方法在可識別性與錯覺強度上顯著超越現有基準方案，成功將視覺字謎從空間維度拓展至時間維度。項目頁面：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/

驚奇之筆：向量素描中的漸進語義幻覺

Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching

摘要

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