The field of image-to-video generation and video editing is rapidly evolving, with a focus on improving the quality and consistency of generated videos. Recent developments have centered around addressing challenges such as conditional image leakage, motion realism, and temporal coherence. Researchers are exploring innovative approaches, including inversion-free methods, Fourier-guided latent shifting, and retrieval-augmented frameworks, to enhance the capabilities of image-to-video models and video editing techniques. Notable papers in this area include: MotionRAG, which proposes a retrieval-augmented framework for motion realism, and FlashI2V, which introduces Fourier-Guided Latent Shifting to prevent conditional image leakage. FreeViS is also noteworthy for its training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence.