Abstract
We propose a novel, zero-shot image generation technique called "Visual Concept Blending" that provides fine-grained control over which features from multiple reference images are transferred to a source image. If only a single reference image is available, it is difficult to isolate which specific elements should be transferred. However, using multiple reference images, the proposed approach distinguishes between common and unique features by selectively incorporating them into a generated output. By operating within a partially disentangled Contrastive Language-Image Pre-training (CLIP) embedding space (from IP-Adapter), our method enables the flexible transfer of texture, shape, motion, style, and more abstract conceptual transformations without requiring additional training or text prompts. We demonstrate its effectiveness across a diverse range of tasks, including style transfer, form metamorphosis, and conceptual transformations, showing how subtle or abstract attributes (e.g., brushstroke style, aerodynamic lines, and dynamism) can be seamlessly combined into a new image. In a user study, participants accurately recognized which features were intended to be transferred. Its simplicity, flexibility, and high-level control make Visual Concept Blending valuable for creative fields such as art, design, and content creation, where combining specific visual qualities from multiple inspirations is crucial.
Abstract (translated)
我们提出了一种新颖的零样本图像生成技术，名为“视觉概念融合”，该技术能够精细控制从多张参考图像中的哪些特征转移到源图像。当仅有一张参考图像可用时，很难确定应转移的具体元素。然而，通过使用多张参考图像，所提出的这种方法可以区分出共同和独特的特征，并有选择地将它们整合到生成的输出中。 我们的方法在部分解耦的对比语言-图像预训练（CLIP）嵌入空间内操作（来自IP适配器），从而能够灵活地转移纹理、形状、运动、风格以及更抽象的概念转化，而无需额外的训练或文本提示。我们在包括样式转换、形态变化和概念转化在内的多种任务上展示了该技术的有效性，并证明了如何将微妙或抽象的属性（例如笔触风格、流线型线条和动态感）无缝地结合到新的图像中。 在一项用户研究中，参与者准确识别出哪些特征是意欲转移的。视觉概念融合方法因其简单性、灵活性以及高层次的控制能力，在艺术、设计和内容创作等创意领域尤为有价值，这些领域需要将多来源灵感中的特定视觉质量进行组合。
URL
https://arxiv.org/abs/2503.21277
