The field of 3D computer graphics is witnessing significant advancements in texture generation and manipulation. Researchers are exploring innovative approaches to generate high-quality textures for 3D objects, with a focus on preserving semantic consistency and realism. One notable direction is the use of diffusion models and generative adversarial networks to create detailed and context-aware textures. Another area of research is the development of methods for texture swapping and transfer between 3D objects, enabling efficient and versatile visual transformations. Furthermore, there is a growing interest in applying these techniques to real-world applications, such as game development and simulation. Noteworthy papers in this area include TriTex, which learns a volumetric texture field from a single textured mesh, and FreeUV, which recovers high-quality 3D facial textures from single-view 2D images without requiring annotated or synthetic data. RomanTex is also notable for its multi-view-based texture generation framework that integrates a multi-attention network with an underlying 3D representation. Additionally, the Progressive Rendering Distillation method has shown promise in adapting stable diffusion models for instant text-to-mesh generation without requiring 3D data.