3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization
SeungJeh Chung JooHyun Park Hyewon Kan HyeongYeop Kang
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.