4K4DGen is a novel framework that generates immersive 4D panoramic environments at 4K resolution. The system takes a static panoramic image and animates specific regions through user interaction, converting them into 4D point-based representations. This enables real-time rendering of novel views and dynamic timestamps, enhancing immersive virtual exploration. The framework introduces a pipeline that facilitates natural scene animations and optimizes 4D Gaussians using efficient splatting techniques for real-time exploration. To address the lack of scene-scale annotated 4D data, a novel Panoramic Denoiser is proposed, adapting generic 2D diffusion priors to animate consistently in 360-degree images, transforming them into panoramic videos with dynamic scenes. Subsequently, the panoramic video is elevated into a 4D immersive environment while preserving spatial and temporal consistency. By transferring prior knowledge from 2D models in the perspective domain to the panoramic domain and using 4D lifting with spatial appearance and geometry regularization, high-quality panorama-to-4D generation is achieved at 4K resolution. The framework addresses two key challenges: animating consistent panoramic videos across a 360-degree field-of-view and preserving spatial and temporal consistency as the panoramic video transitions into a 4D environment. The system demonstrates superior performance compared to existing methods, achieving higher CLIP similarity scores and user evaluations. The research has implications for various applications including AR/VR, movie production, and video games, but also raises concerns about potential misuse in creating deceptive content or privacy violations.4K4DGen is a novel framework that generates immersive 4D panoramic environments at 4K resolution. The system takes a static panoramic image and animates specific regions through user interaction, converting them into 4D point-based representations. This enables real-time rendering of novel views and dynamic timestamps, enhancing immersive virtual exploration. The framework introduces a pipeline that facilitates natural scene animations and optimizes 4D Gaussians using efficient splatting techniques for real-time exploration. To address the lack of scene-scale annotated 4D data, a novel Panoramic Denoiser is proposed, adapting generic 2D diffusion priors to animate consistently in 360-degree images, transforming them into panoramic videos with dynamic scenes. Subsequently, the panoramic video is elevated into a 4D immersive environment while preserving spatial and temporal consistency. By transferring prior knowledge from 2D models in the perspective domain to the panoramic domain and using 4D lifting with spatial appearance and geometry regularization, high-quality panorama-to-4D generation is achieved at 4K resolution. The framework addresses two key challenges: animating consistent panoramic videos across a 360-degree field-of-view and preserving spatial and temporal consistency as the panoramic video transitions into a 4D environment. The system demonstrates superior performance compared to existing methods, achieving higher CLIP similarity scores and user evaluations. The research has implications for various applications including AR/VR, movie production, and video games, but also raises concerns about potential misuse in creating deceptive content or privacy violations.