The field of 3D human reconstruction and generation is moving towards more efficient and accurate methods for reconstructing and generating high-quality 3D models from sparse and uncalibrated data. Recent developments have focused on feed-forward models that can reconstruct and interpolate 3D humans in real-time, as well as unified frameworks that encode geometry and appearance in a single latent space. These advancements have enabled the generation of high-fidelity 3D assets and have improved the quality of 3D stylization and synthesis. Notable papers in this area include Forge4D, which proposes a feed-forward 4D human reconstruction and interpolation model, and UniLat3D, which introduces a unified framework for single-stage 3D generation. Additionally, Stylos presents a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content. These papers demonstrate significant improvements in the field and have the potential to impact various downstream applications.