The field of video generation and image synthesis is rapidly evolving, with a focus on improving the quality, efficiency, and temporal consistency of generated content. Recent developments have centered around the use of diffusion models, which have shown promise in generating high-quality videos with superior temporal consistency. These models have been extended to various applications, including video inpainting, video synthesis, and image synthesis. Notably, researchers have explored the use of spherical latent representations to mitigate distortions in 360-degree panoramic content generation, and hierarchical synthesis frameworks to improve the efficiency of high-resolution video generation. Additionally, there has been a push towards developing more efficient and practical models, such as those using knowledge distillation strategies and compressed latent spaces. The development of new loss functions, such as flow-conditioned loss strategies, has also improved the motion stability and coherence of generated videos. Furthermore, the extension of physically based rendering materials to incorporate reflection and transmission properties has enabled the synthesis of realistic images with complex surfaces. Some noteworthy papers include: SphereDiff, which introduces a novel approach for seamless 360-degree panoramic image and video generation, and Turbo2K, which proposes an efficient framework for generating detail-rich 2K videos. DiTPainter is also notable for its efficient video inpainting model based on diffusion transformers.