The current trajectory in video generation research is marked by a significant shift towards open-source, large-scale models that aim to democratize access to advanced video creation technologies. Researchers are increasingly focusing on developing datasets and frameworks that not only enhance the quality and diversity of generated videos but also ensure that these models can be effectively trained and scaled. The emphasis is on integrating multi-modal data, such as text, audio, and skeletal sequences, to improve the alignment and coherence of generated content. Additionally, there is a growing recognition of the importance of ethical considerations and regulatory compliance in the deployment of generative AI models, particularly in industries like media and entertainment. Noteworthy advancements include the introduction of high-quality, human-centric datasets that significantly improve the realism and accuracy of generated videos, as well as the development of systematic frameworks that enable the training of models with billions of parameters, rivaling the performance of proprietary models. These developments collectively push the boundaries of what is possible in video generation, fostering a more inclusive and innovative research ecosystem.