The recent advancements in the field of human-centric video generation and editing have shown a significant shift towards more controllable and realistic outputs. Researchers are increasingly leveraging multimodal data and advanced machine learning techniques to enhance the quality and naturalness of generated content. A notable trend is the use of large language models (LLMs) to guide and direct the generation process, ensuring that the resulting videos align closely with textual descriptions and user instructions. This approach not only improves the fidelity of human motion and interactions but also enhances the overall coherence and realism of the scenes. Additionally, there is a growing emphasis on zero-shot learning and scalable pipelines, which allow for the customization of videos without the need for extensive fine-tuning or additional datasets. These innovations are paving the way for more intuitive and efficient video editing tools, making it easier for users to create complex and dynamic content. Notably, models like SUGAR and DirectorLLM have demonstrated exceptional performance in identity preservation and video-text alignment, setting new benchmarks in the field. Furthermore, the introduction of frameworks such as MIVE highlights the importance of addressing multi-instance video editing challenges, ensuring precise and faithful edits across diverse scenarios.