The field of 4D content generation and editing is rapidly advancing, with a focus on developing innovative methods for creating and manipulating dynamic 3D scenes. Recent research has explored the use of diffusion models, Gaussian feature fields, and feature banks to improve the quality and consistency of generated content. Notably, several papers have proposed training-free approaches for 4D scene generation, motion editing, and omnimatte decomposition, which offer significant advantages in terms of efficiency and generalizability. These advancements have the potential to enable real-time, controllable rendering and editing of complex 3D scenes, and could have a major impact on applications such as computer vision, graphics, and video production. Noteworthy papers include MotionDiff, which proposes a training-free zero-shot diffusion method for interactive motion editing, and OmnimatteZero, which presents a training-free approach for omnimatte decomposition using pre-trained video diffusion models. Feature4X is also notable for its universal framework for extending 2D vision foundation models to the 4D realm.