The video Motion Prompting research by Google DeepMind controls video generation through motion trajectories, with the core objective of designing a generative system that uses motion information to generate dynamic video content in a more flexible and controllable manner.

Train trajectory-based conditional video generation models.

• to represent motion:
• This flexible motion representation supports encoding single or multiple point trajectories.
• It can describe the motion of specific objects or the entire scene.
• Including occluded and temporally sparse motion sequences.

Method steps

1. Train trajectory-based conditional video generation models.

   

2. Use motion prompts to guide the model to generate target behaviors.

   

Comparative analysis