3D stylization, which entails the application of specific styles to three-dimensional objects, holds significant commercial potential as it enables the creation of diverse 3D objects with distinct moods and styles, tailored to specific demands of different scenes. With recent advancements in text-driven methods and artificial intelligence, the stylization process is increasingly intuitive and automated, thereby diminishing the reliance on manual labor and expertise. However, existing methods have predominantly focused on holistic stylization, thereby leaving the application of styles to individual components of a 3D object unexplored. In response, we introduce 3DStyleGLIP, a novel framework specifically designed for text-driven, part-tailored 3D stylization. Given a 3D mesh and a text prompt, 3DStyleGLIP leverages the vision-language embedding space of the Grounded Language-Image Pre-training (GLIP) model to localize the individual parts of the 3D mesh and modify their colors and local geometries to align them with the desired styles specified in the text prompt. 3DStyleGLIP is effectively trained for 3D stylization tasks through a part-level style loss working in GLIP's embedding space, supplemented by two complementary learning techniques. Extensive experimental validation confirms that our method achieves significant part-wise stylization capabilities, demonstrating promising potential in advancing the field of 3D stylization.

翻译：三维风格化是指将特定风格应用于三维对象的技术，具有显著的商业潜力，因为它能够根据不同场景的特定需求，创建具有不同氛围和风格的三维对象。随着文本驱动方法和人工智能的最新进展，风格化过程日益直观和自动化，从而减少了对人工和专业知识的依赖。然而，现有方法主要集中在对三维对象进行整体风格化，尚未探索将风格应用于三维对象各个部件的方法。为此，我们提出3DStyleGLIP，一种专为文本驱动的部件级三维风格化设计的新框架。给定三维网格和文本提示，3DStyleGLIP利用基础语言-图像预训练（GLIP）模型的视觉-语言嵌入空间，定位三维网格的各个部件，并修改其颜色和局部几何形状，使其与文本提示中指定的所需风格对齐。通过基于GLIP嵌入空间的部件级风格损失，并结合两种互补学习技术，3DStyleGLIP能够有效训练用于三维风格化任务。大量实验验证表明，我们的方法实现了显著的部件级风格化能力，在推动三维风格化领域发展方面展现出巨大潜力。