T2V Generation

Text-to-video (T2V) generation aims to synthesize realistic videos from textual descriptions, a challenging task currently addressed using diffusion models. Research focuses on improving the compositional capabilities of these models, handling complex scenarios with multiple objects and dynamic actions, and developing robust evaluation metrics that capture video dynamics and semantic accuracy. These advancements are significant for various applications, including film production, animation, and virtual reality, and are driving improvements in both model architectures and evaluation methodologies within the broader field of generative AI.