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Using OpenPose with ControlNet

Updated: Jul 10, 2023

Revolutionizing Pose Annotation in Generative Images: A Guide to Using OpenPose with ControlNet and A1111


Let's talk about pose annotation. It's a big deal in computer vision and AI. Think animation, game design, healthcare, sports. But getting it right is tough. Complex human poses can be tricky to generate accurately.


Enter OpenPose and ControlNet — two powerful AI tools that are changing the game when used together. In this guide, we'll see how they team up to make generating images using a certain pose a breeze.


Meet OpenPose


Developed by the smart folks at Carnegie Mellon University, OpenPose is a real-time pose estimation framework that uses deep learning to detect and track human body key-points in images and videos.


It identifies body parts, facial landmarks, and hand key-points, providing a detailed representation of human poses. It is capable of handling multiple people in a single frame and is robust to occlusions. In this context, An occlusion is the partial or complete obstruction of an object or body part in an image or video, making it difficult to see or analyze.


OpenPose is widely used in various applications, including virtual reality, human-computer interaction, animation, gaming, healthcare, and sports analysis.


Say Hello to ControlNet

ControlNet enhances the capabilities of Stable Diffusion Models, allowing users to have greater control over the image generation process by providing additional input conditions, such as depth maps or pose key points. This results in more precise and accurate image generation that adheres to specific compositional elements.


Putting OpenPose and ControlNet Together

So, how do OpenPose and ControlNet work together? Imagine using Open Pose to define an arbitrary pose, and then using ControlNet to generate an image. Like this:



Let's break it down step by step. Here's a video tutorial I put together.



Requirements: A RunPod account

  1. To follow along with my video, create a RunPod account: https://runpod.io/


Set Up: Getting OpenPose and ControlNet up and running is simple using Automatic 1111.

  1. Open Automatic 1111

  2. Go to the Extensions tab

  3. Paste this extension: https://github.com/fkunn1326/openpose-editor

  4. Click on Install & Reload UI

Control It: Creating poses right in Automatic 1111

  1. Click the new tab titled "OpenPose Editor"

  2. Click and drag the keypoints to pose the model

  3. Click "Send to ControlNet"

Generate: Let ControlNet work its magic.

  1. Enter your prompt

  2. Enable the ControlNet option

  3. From models, chose the OpenPose model

  4. Click "Generate"

Fine-Tune: Need to make adjustments? ControlNet lets you tweak the results for that perfect image.


In no time, you'll have AI-generated images that match your pose and conditions.


Real-World Magic

OpenPose and ControlNet aren't just cool tech—they're practical tools with real-world applications. Here are a few examples:

  • Animation: Bring characters to life with accurate and expressive poses.

  • Game Design: Level up your game design with realistic character movements.

  • Sports: Generate images of athletes in realistic poses.

The possibilities are endless.


Why It Matters

Pose annotation is more than just a technical challenge—it's a creative opportunity. With OpenPose and ControlNet, artists, designers, and researchers can unlock new levels of expression and accuracy. It's about capturing the essence of human movement and bringing ideas to life.


And the best part? OpenPose and ControlNet are accessible to everyone. Whether you're a seasoned pro or a curious beginner, you can start experimenting and creating today.


In Conclusion

OpenPose and ControlNet are revolutionizing pose annotation. Together, they offer a powerful and flexible solution for AI image generation. So go ahead—give them a try. Explore, experiment, and discover what's possible. With OpenPose and ControlNet, the future of pose annotation is in your hands.


Happy posing!

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