Diffusion Based Video Generation

Diffusion-based video generation aims to create realistic and controllable videos using diffusion models, primarily focusing on improving temporal coherence, generating longer videos, and enabling fine-grained control over content. Current research emphasizes novel architectures that decompose video signals into common and unique components, leverage autoregressive models and motion cues for temporal consistency, and incorporate diverse conditioning mechanisms such as text, audio, 3D avatars, bounding boxes, and masks to achieve precise control. This rapidly advancing field holds significant potential for applications in entertainment, animation, virtual and augmented reality, and beyond, while also posing challenges in detecting AI-generated videos and ensuring ethical considerations.