Transferring 2D textures onto complex 3D scenes plays a vital role in enhancing the efficiency and controllability of 3D multimedia content creation. However, existing 3D style transfer methods primarily focus on transferring abstract artistic styles to 3D scenes. These methods often overlook the geometric information of the scene, which makes it challenging to achieve high-quality 3D texture transfer results. In this paper, we present GT2-GS, a geometry-aware texture transfer framework for gaussian splatting. First, we propose a geometry-aware texture transfer loss that enables view-consistent texture transfer by leveraging prior view-dependent feature information and texture features augmented with additional geometric parameters. Moreover, an adaptive fine-grained control module is proposed to address the degradation of scene information caused by low-granularity texture features. Finally, a geometry preservation branch is introduced. This branch refines the geometric parameters using additionally bound Gaussian color priors, thereby decoupling the optimization objectives of appearance and geometry. Extensive experiments demonstrate the effectiveness and controllability of our method. Through geometric awareness, our approach achieves texture transfer results that better align with human visual perception.

Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any video diffusion model remains a challenging problem. In this paper, we propose a novel zero-shot moving object trajectory control framework, Motion-Zero, to enable arbitrary single-object-trajectory control for the text-to-video diffusion model. To this end, an initial noise prior module is designed to provide a position-based prior to improve the stability of the appearance of the moving object and the accuracy of position. In addition, based on the attention map of the U-Net, spatial constraints are directly applied to the denoising process of diffusion models, which further ensures the positional consistency of moving objects during the inference. Furthermore, temporal consistency is guaranteed with a proposed shift temporal attention mechanism. Our method can be flexibly applied to various state-of-the-art video diffusion models without any training process. Extensive experiments demonstrate our proposed method can control the motion trajectories of arbitrary objects while preserving the original ability to generate high-quality videos.