The field of video processing and generation is rapidly advancing with the development of new models and techniques. A key direction in the field is the use of diffusion-based models, which have shown impressive results in tasks such as video super-resolution and video generation. These models are able to effectively capture the complex patterns and structures present in video data, allowing for high-quality outputs. Another significant trend is the use of transformer-based architectures, which have proven to be highly effective in modeling the temporal and spatial relationships present in video data. Noteworthy papers in this regard include OutDreamer, which introduces a novel video outpainting framework, and VSRM, which proposes a robust Mamba-based framework for video super-resolution. Additionally, papers such as STR-Match, MoMa, and SIEDD have made significant contributions to the field, advancing the state-of-the-art in video editing, video recognition, and video compression.