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Vibe Spaces for Creatively Connecting and Expressing Visual Concepts

About

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.

Huzheng Yang, Katherine Xu, Andrew Lu, Michael D. Grossberg, Yutong Bai, Jianbo Shi• 2025

Related benchmarks

TaskDatasetResultRank
Image BlendingTotally Looks Like High Difficulty
AMD Score (Input 1)0.632
8
Image BlendingTotally Looks Like Low Difficulty
Attribute-Masked DreamSim (Input 1)0.708
8
Continuous BlendingMorph4Data
Perplexity (PPL)61.96
5
Continuous BlendingBlendBench
PPL252.5
5
Image BlendingTotally Looks Like High Difficulty
Human Preference Score60
4
Image BlendingTotally Looks Like Medium Difficulty
Human Preference Score50
4
Image BlendingArchitecture (test)
Human Preference42
4
Image BlendingTotally Looks Like
CLIP Score0.223
4
Image BlendingArchitecture
CLIP Score0.15
4
Image BlendingTotally Looks Like Low Difficulty
Human Preference Score26.7
4
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Other info

GitHub

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