灵感种子：学习用于生成式探索的非字面视觉组合
Inspiration Seeds: Learning Non-Literal Visual Combinations for Generative Exploration
AI总结：
提出Inspiration Seeds框架，通过CLIP稀疏自编码器提取编辑方向并隔离概念对，实现无需文本提示的两张输入图像的视觉组合生成，支持早期创意阶段的探索性构思。
摘要：
虽然生成模型已成为图像合成的强大工具，但它们通常针对执行精心设计的文本提示进行优化，对于想法形成之前常见的开放式视觉探索支持有限。相比之下，设计师经常从松散连接的视觉参考中汲取灵感，寻找能激发新想法的涌现连接。我们提出了Inspiration Seeds，这是一个将图像生成从最终执行转变为探索性构思的生成框架。给定两张输入图像，我们的模型生成多样且视觉连贯的组合，揭示输入之间的潜在关系，而无需依赖用户指定的文本提示。我们的方法是前馈式的，在完全通过视觉手段分解的合成三元组上训练：我们使用CLIP稀疏自编码器在CLIP潜在空间中提取编辑方向并隔离概念对。通过消除对语言的依赖并支持快速、直观的重组，我们的方法支持在创意工作的早期和模糊阶段进行视觉构思。
英文摘要：
While generative models have become powerful tools for image synthesis, they are typically optimized for executing carefully crafted textual prompts, offering limited support for the open-ended visual exploration that often precedes idea formation. In contrast, designers frequently draw inspiration from loosely connected visual references, seeking emergent connections that spark new ideas. We propose Inspiration Seeds, a generative framework that shifts image generation from final execution to exploratory ideation. Given two input images, our model produces diverse, visually coherent compositions that reveal latent relationships between inputs, without relying on user-specified text prompts. Our approach is feed-forward, trained on synthetic triplets of decomposed visual aspects derived entirely through visual means: we use CLIP Sparse Autoencoders to extract editing directions in CLIP latent space and isolate concept pairs. By removing the reliance on language and enabling fast, intuitive recombination, our method supports visual ideation at the early and ambiguous stages of creative work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
