The field of text-to-3D generation and diffusion models is rapidly advancing, with a focus on improving the quality and efficiency of generated 3D assets. Recent developments have highlighted the importance of semantic consistency, texture realism, and geometric accuracy in generated models. Researchers are exploring new approaches to address the limitations of existing methods, such as score distillation and denoising score matching. Notably, innovative methods like AnchorDS and Target-Balanced Score Distillation have shown significant improvements in generation quality and efficiency. Furthermore, the role of embedding geometry in image interpolation has been investigated, leading to smoother and more coherent intermediate images. Additionally, generalized denoising diffusion codebook models have been proposed to extend the applicability of diffusion models to various tasks. Noteworthy papers include: AnchorDS, which introduces an improved score distillation mechanism for text-to-3D generation, producing finer-grained details and stronger semantic consistency. Target-Balanced Score Distillation, which resolves the trade-off between texture optimization and shape distortion in 3D asset generation, yielding high-fidelity textures and geometrically accurate shapes.