创意连接与视觉概念表达的沉浸式空间

摘要

创造新颖的视觉概念往往需要通过图像最相关的共享属性——即其“氛围”——来连接不同概念。我们提出“氛围融合”这一新任务，旨在生成连贯且有意义的混合图像，以揭示图像间的共性特征。现有方法在识别并遍历潜空间中连接远距离概念的非线性路径时存在困难，而实现优质融合正面临这一挑战。为此我们构建了“氛围空间”，这是一种在CLIP等特征空间中学习低维测地线的分层图流形，能够实现概念间平滑且语义一致的过渡。为评估创意质量，我们设计了融合人类判断、大语言模型推理与基于几何路径的难度分数的认知启发式框架。实验表明，相较于现有方法，人类评价者一致认为氛围空间生成的融合图像更具创意与连贯性。

English

Creating new visual concepts often requires connecting distinct ideas through their most relevant shared attributes -- their vibe. We introduce Vibe Blending, a novel task for generating coherent and meaningful hybrids that reveals these shared attributes between images. Achieving such blends is challenging for current methods, which struggle to identify and traverse nonlinear paths linking distant concepts in latent space. We propose Vibe Space, a hierarchical graph manifold that learns low-dimensional geodesics in feature spaces like CLIP, enabling smooth and semantically consistent transitions between concepts. To evaluate creative quality, we design a cognitively inspired framework combining human judgments, LLM reasoning, and a geometric path-based difficulty score. We find that Vibe Space produces blends that humans consistently rate as more creative and coherent than current methods.

创意连接与视觉概念表达的沉浸式空间

Vibe Spaces for Creatively Connecting and Expressing Visual Concepts

摘要

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