Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Use Google DeepMind VEO3 for Video Creation + Zone Counting with Ultralytics Python package 🚀

Explore Google DeepMind’s VEO3 model for AI video generation and combine it with Ultralytics' zone counting solution. This tutorial guides you through the VEO3 homepage, visualizations, and examples of prompt-based video generation. We create sample videos, such as those of bottles on a conveyor belt and busy roads with vehicles, and then apply Ultralytics’ region-counting solution to track objects in different regions of videos in real-time. 💡You’ll also see how to refine prompts for better AI video results and integrate VEO3 outputs with computer vision workflows. Chapters 00:00 - Introduction to Google DeepMind VEO3 00:42 - Exploring VEO3 homepage and Genie 3 quick look 01:22 - VEO3 video example: Bottles on conveyor belt 02:23 - Overview of zone counting solution 03:45 - VEO3 video example: Busy road with vehicles 04:06 - Zone counting on conveyor belt video (VEO3 output) 06:04 - Refining prompts for better VEO3 video results 06:54 - Zone counting on busy road video (VEO3 output) 07:40 - Conclusion and key takeaways 🔗 VEO3 documentation ➡️ https://deepmind.google/models/veo/ Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #googledeepmind #veo3 #aivideo #ultralytics #objectcounting #computervision