The field of 3D Gaussian Splatting and textile generation is rapidly evolving, with a focus on improving efficiency, accuracy, and generality. Researchers are developing new methods to compress and reconstruct 3D scenes, garments, and textiles, leveraging techniques such as sparse representation, point cloud encoding, and diffusion models. Notably, innovations in 3D Gaussian Splatting data compression and textile classification via hyperspectral imaging are enabling faster and more accurate processing of 3D data. Furthermore, advances in text-to-3D generation and editing are bridging the gap between 2D and 3D content creation. Notable papers in this area include: HybridGS, which presents a high-efficiency 3DGS compression framework using dual-channel sparse representation and point cloud encoder. Rethinking Score Distilling Sampling for 3D Editing and Generation, which proposes Unified Distillation Sampling, a method that seamlessly integrates both the generation and editing of 3D assets.