Motion Prompting ; Breakthrough in Video Generation from Google DeepMind ✨📹

✨ A breakthrough in video generation: Motion Prompting! 📹 from Google DeepMind

This approach leverages spatio-temporal motion trajectories as conditioning signals, giving users flexible control over video content like object and camera movements. 🎮🎥

Here’s how it works:

  • ControlNet adapter trained on a pre-trained video diffusion model powers this flexibility.
  • Motion prompt expansion translates high-level user requests into detailed motion trajectories, making complex tasks feel effortless.

The results?

  • Diverse applications with satisfactory outcomes
  • Emergent behaviors like realistic physics simulations 🌌⚙️

Paper
Github


#AI #VideoGeneration #MotionPrompting #Innovation #ControlNet #VideoDiffusion #CreativeAI #Google #Deepmind #GenerativeAI #GenAI #LLM #LMMM




Enjoy Reading This Article?

Here are some more articles you might like to read next:

  • Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra
  • Displaying External Posts on Your al-folio Blog
  • AlphaGo Moment for Model Architecture Discovery ; The Rise of Autonomous AI Scientists 🤖🚀
  • Reinforcement pre-training - baking the cherry into the cake
  • Group Sequence Policy Optimization (GSPO); A Smarter Approach to RL for LLMs and MoE Models