Contemporary makeup transfer methods primarily focus on replicating makeup from one face to another, considerably limiting their use in creating diverse and creative character makeup essential for visual storytelling. Such methods typically fail to address the need for uniqueness and contextual relevance, specifically aligning with character and story settings as they depend heavily on existing facial makeup in reference images. This approach also presents a significant challenge when attempting to source a perfectly matched facial makeup style, further complicating the creation of makeup designs inspired by various story elements, such as theme, background, and props that do not necessarily feature faces. To address these limitations, we introduce $Gorgeous$, a novel diffusion-based makeup application method that goes beyond simple transfer by innovatively crafting unique and thematic facial makeup. Unlike traditional methods, $Gorgeous$ does not require the presence of a face in the reference images. Instead, it draws artistic inspiration from a minimal set of three to five images, which can be of any type, and transforms these elements into practical makeup applications directly on the face. Our comprehensive experiments demonstrate that $Gorgeous$ can effectively generate distinctive character facial makeup inspired by the chosen thematic reference images. This approach opens up new possibilities for integrating broader story elements into character makeup, thereby enhancing the narrative depth and visual impact in storytelling.

翻译：当代妆容迁移方法主要侧重于将一张脸部的妆容复制到另一张脸上，这极大地限制了其在视觉叙事中创造多样化、富有创意的角色妆容方面的应用。此类方法通常无法满足对独特性和情境相关性的需求，特别是与角色及故事情节设定保持一致，因为它们高度依赖于参考图像中已有的面部妆容。在试图找到一种完美匹配的面部妆容风格时，这种方法也带来了显著挑战，使得从各种故事元素（如主题、背景以及不一定包含人脸的道具）中汲取灵感来设计妆容变得更加复杂。为应对这些局限，我们提出了$Gorgeous$，一种新颖的基于扩散模型的妆容应用方法，它超越了简单的迁移，能够创新性地打造独特且具主题性的面部妆容。与传统方法不同，$Gorgeous$无需在参考图像中出现人脸。相反，它从最少三到五张任意类型的图像中汲取艺术灵感，并将这些元素直接转化为可在面部应用的实用妆容。我们全面的实验表明，$Gorgeous$能够有效地生成受所选主题参考图像启发的独特角色面部妆容。这种方法为将更广泛的故事元素融入角色妆容开辟了新的可能性，从而增强了叙事中的故事深度和视觉冲击力。