The recent advancements in the field of 3D avatar generation and rendering have shown significant progress, particularly in the areas of generative models, real-time performance, and photorealism. Researchers are increasingly focusing on developing methods that can create high-fidelity, animatable 3D avatars from limited input data, such as single images or sparse-view videos. These methods often leverage novel neural network architectures, such as diffusion models and graph neural networks, to enhance the quality and efficiency of avatar generation. Additionally, there is a strong emphasis on reducing computational overhead and storage requirements, making these technologies more accessible for real-time applications like virtual reality and gaming. The integration of multi-view diffusion models and pose-conditioned denoising techniques has notably improved the consistency and detail of 3D reconstructions, while advancements in rendering pipelines have enabled faster and more efficient processing on consumer-grade hardware. Notably, some of the most innovative contributions include the development of compact, high-quality avatar representations using Gaussian splatting and the use of unsupervised learning to bridge domain gaps in character reconstruction. These developments collectively push the boundaries of what is possible in real-time, photorealistic 3D avatar generation, opening new avenues for immersive experiences in various applications.