The field of 3D human generation and pose estimation is rapidly advancing, with a focus on creating highly realistic and animatable 3D avatars. Recent research has explored the use of diffusion models, transformers, and graph attention mechanisms to improve the accuracy and efficiency of these systems. Notably, the integration of 3D geometric guidance and pose-conditioned models has enabled more precise control over human identity, body shape, and animation readiness. Furthermore, the development of lightweight and real-time capable models has expanded the potential applications of 3D human generation and pose estimation. Some of the key innovations in this area include the use of pyramid-structured long-range dependencies, compositional 3D Gaussian Splats refinement, and autonomous verification loops. Overall, these advancements have significant implications for fields such as computer graphics, vision, and robotics. Noteworthy papers include: AdaHuman, which generates high-fidelity animatable 3D avatars from a single in-the-wild image. HuGeDiff, which presents a weakly supervised pipeline for 3D human generation via diffusion with Gaussian Splatting, achieving orders-of-magnitude speed-ups and improved text-prompt alignment. SmartAvatar, which leverages vision-language models to deliver high-quality, customizable avatars with fine-grained control over facial and body features.