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Image-to-video synthesis

As explained in Generative AI in Action

Image-to-video synthesis is the generation of video content from a source image by modeling visual features and motion.

Image-to-video synthesis generates a video from a source image rather than a text prompt. The model associates features in the image with visual concepts and produces video content that matches them. section 5.4

Video-generation approaches differ by their conditioning input:

  • Text-to-video synthesis starts with a written prompt. section 5.4
  • Video-to-video synthesis transforms an existing video while preserving its underlying structure. section 5.4
  • GAN-based video generation uses a generative adversarial network. section 5.4

Tools and visual context

AI video generators such as Pictory support workflows using text, images, and video. Figure 5.15 provides a visual example of Wochit AI video generation. section 5.4

How Generative AI in Action covers it

How deep it goes
Defines the conditioning method and learned visual associations but stops short of implementation details or mathematical derivation.
Examples
References Pictory, Wochit, Sora, and synthetic-data applications for self-driving cars and theme-park design.
What you can do after
Evaluate image-conditioned video generation alongside prompt-, video-, and GAN-based alternatives.
How it connects
Contrasts image-to-video synthesis with text-to-video synthesis, video-to-video synthesis, and GAN-based video generation.
Cover of Generative AI in Action
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.