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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Blending | Totally Looks Like High Difficulty | AMD Score (Input 1)0.632 | 8 | |
| Image Blending | Totally Looks Like Low Difficulty | Attribute-Masked DreamSim (Input 1)0.708 | 8 | |
| Continuous Blending | Morph4Data | Perplexity (PPL)61.96 | 5 | |
| Continuous Blending | BlendBench | PPL252.5 | 5 | |
| Image Blending | Totally Looks Like High Difficulty | Human Preference Score60 | 4 | |
| Image Blending | Totally Looks Like Medium Difficulty | Human Preference Score50 | 4 | |
| Image Blending | Architecture (test) | Human Preference42 | 4 | |
| Image Blending | Totally Looks Like | CLIP Score0.223 | 4 | |
| Image Blending | Architecture | CLIP Score0.15 | 4 | |
| Image Blending | Totally Looks Like Low Difficulty | Human Preference Score26.7 | 4 |