The recent advancements in video understanding and generation have significantly focused on enhancing the granularity and contextual accuracy of video datasets and models. Researchers are increasingly prioritizing the development of large-scale, high-quality video datasets that feature detailed annotations and superior video quality, aiming to improve the consistency between fine-grained conditions and video content. This trend is evident in the creation of datasets that not only capture complex human actions but also integrate multiple perspectives and temporal dynamics, thereby challenging existing models to perform better in recognizing fine-grained motor behaviors and understanding rapid changes in human motion. Additionally, there is a growing emphasis on the use of large language models (LLMs) and multimodal models to generate diverse and fine-grained captions, which are crucial for improving text-video alignment and video moment localization. These innovations are paving the way for more sophisticated video-text retrieval and temporal grounding models, capable of understanding and generating unique captions that accurately describe specific video segments. Furthermore, the integration of motion-focused video-language representations is emerging as a key area, with models now capable of learning from captions that describe the movement and temporal progression of objects, enhancing their performance in various downstream tasks, especially in scenarios with limited data. Overall, the field is moving towards more nuanced and context-aware video understanding, driven by advancements in dataset quality, model architecture, and the integration of multimodal learning approaches.