Learn how to perform pose estimation using Ultralytics YOLO and Google MediaPipe. This tutorial walks you through both frameworks, comparing their keypoints, model variants, and real-time demos. We’ll cover the documentation, show code walkthroughs for each, and demonstrate pose estimation on both sample videos and webcam input. By the end, you’ll understand the strengths of YOLO Pose and MediaPipe, how they differ, and where each framework can be applied in real-world scenarios like fitness tracking, gesture recognition, and human movement analysis. Chapters 00:00 - Introduction to Ultralytics YOLO Pose and MediaPipe 00:20 - Exploring YOLO Pose documentation 01:04 - Understanding YOLO Pose keypoints 01:24 - Choosing between YOLO Pose model variants (n/s/m/l/x) 01:41 - Exploring MediaPipe Pose documentation 02:32 - Understanding MediaPipe Pose keypoints 02:50 - YOLO Pose vs MediaPipe: key differences in code and approach 03:44 - Implementing pose estimation with MediaPipe (code walkthrough) 05:17 - Implementing pose estimation with YOLO (code walkthrough) 06:25 - MediaPipe Pose estimation demo on sample video 07:59 - YOLO Pose estimation demo on sample video 08:40 - Real-time pose estimation with YOLO and MediaPipe (webcam demo) 09:46 - Final thoughts: Which pose estimation framework should you use? 🔗 Code ➡️ Ultralytics Resources: 🏢 About Us: 💼 Join Our Team: 📞 Contact Us: 💬 Discord Community: 📄 Ultralytics License: #poseestimation #ultralytics #mediapipe #computervision #machinelearning