The field of text-to-video generation is rapidly advancing, with a focus on improving the control and quality of generated videos. Recent developments have centered on enhancing the ability to manipulate and refine video content, including the use of localized text control signals, iterative self-improvement, and cross-stage prompt optimization. These innovations have led to significant gains in visual fidelity, text alignment, and motion controllability. Notably, the introduction of frameworks that condition video generation on trajectories paired with localized text descriptions has enabled more precise control over the subject composition of generated scenes. Furthermore, the development of multi-agent systems that autonomously improve video generation through refining prompts has shown promising results. Overall, the field is moving towards more holistic and automated approaches to video generation, with a focus on creating coherent and engaging narratives. Noteworthy papers include: TGT, which introduces a framework for conditioning video generation on trajectories paired with localized text descriptions, achieving higher visual quality and more accurate text alignment. VISTA, a multi-agent system that autonomously improves video generation through refining prompts, consistently improving video quality and alignment with user intent. RAPO++, a cross-stage prompt optimization framework that substantially improves text-to-video generation without modifying the underlying generative backbone, achieving significant gains in semantic alignment and compositional reasoning. HoloCine, a model that generates entire scenes holistically to ensure global consistency, achieving precise directorial control and remarkable emergent abilities.