通过学习视觉概念间的语感路径，实现更自然的图像混合创作。
Vibe Spaces for Creatively Connecting and Expressing Visual Concepts
- 构建层次化图流形，捕捉特征空间中的低维测地线路径。
- 生成的混合图像在人类评估中更连贯且更具创意性。
- 适合艺术创作与跨概念视觉联想的研究者使用。
创造新视觉概念常需通过共享属性——即‘语感’——连接不同想法。我们提出Vibe Blending任务，旨在生成语义连贯且有意义的图像混合体，揭示图像间的核心共性。现有方法难以识别并穿越潜在空间中相距甚远概念间的非线性路径。为此，我们提出Vibe Space，一种在CLIP等特征空间中学习低维测地线的分层图流形，实现概念间平滑且语义一致的过渡。为评估创造力，设计融合人类判断、大模型推理与几何路径难度评分的认知启发式框架。实验表明，Vibe Space生成的混合图像在人类评价中持续优于现有方法，具有更高创造性与连贯性。
原文摘要 · Abstract (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.
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