The recent advancements in the field of video generation and animation have shown a significant shift towards more controllable and flexible models. Researchers are focusing on enhancing the integration of content and style in motion generation, often through bidirectional control flows that mitigate conflicts between these elements. This approach not only preserves the dynamics of style but also extends control to multiple modalities like text and images, enabling more nuanced and diverse outputs. Additionally, there is a strong emphasis on improving temporal stability in video interpolation, with methods that provide explicit frame-wise conditions to ensure coherent transitions, even in the presence of large motion gaps. Self-supervised techniques for video denoising are also advancing, leveraging optical flow alignment and global frame feature utilization to achieve superior performance without ground truth data. Noteworthy contributions include models that allow for flexible controls in video inbetweening, enabling dynamic and customizable visual narratives, and those that optimize multi-frame interpolation for medical imaging, enhancing accuracy and noise suppression. Overall, the field is progressing towards more sophisticated, multi-modal, and contextually accurate video generation and animation techniques.