CosGaussian: Towards Text-to-3D Semantically Controllable 3D Object Style Transfer with Gaussian Splatting

With the rapid advancement of 3D technologies, 3D editing techniques have become increasingly important, among which 3D stylization serves as a crucial tool for editing 3D surfaces. However, semantically controllable object-level 3D stylization remains an open challenge. In this article, we propose CosGaussian, a framework based on 3DGS that enables precise control of the transfer style in object-level. Firstly, by leveraging Vision-Language Models (VLMs), CosGaussian can effectively extract the stylization intentions of textual input from user and generate corresponding style reference image for subsequent stylization. Then, we introduce Adaptive Feature Transfer (AFT), a semantics-aware style transfer method that can accurately transfer the local style from style reference image to objects in 3D scenes. Experiments demonstrate that CosGaussian outperforms state-of-the-art methods in both stylization accuracy and quality, presenting a robust, user-friendly solution for semantically controllable 3D style transfer.