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.

介绍了一种基于文本驱动的、部分定制的3D风格化的新方法3DStyleGLIP，该方法利用Grounded Language-Image Pre-training (GLIP) 模型的视觉-语言嵌入空间来定位3D模型的各个部分，并按照文本提示中指定的风格修改它们的颜色和局部几何形状，通过在GLIP的嵌入空间中使用部分级风格损失和两种补充学习技术，有效地训练了3DStyleGLIP，实验证实了该方法在部分级别的风格化任务上取得了显著成果，显示出推动3D风格化领域发展的潜力。

3DStyleGLIP：面向部分的文本引导的3D神经风格化