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。