Image to Video Generation

"Image to Video Generation" refers to the task of generating a sequence of video frames based on a single static image or a set of static images. The goal is to produce videos that are coherent in appearance, motion, and style, and are temporally ordered. This task typically employs deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), which are trained on large-scale video datasets to enable the model to generate plausible video frames from input images and other auxiliary information. This technology has significant applications in video synthesis, virtual reality, and augmented reality.