ControlNet is a neural network architecture that can be used to control pretrained large diffusion models to support additional input conditions. The purpose of ControlNet-openpose is to enable conditional input of human pose keypoints to control large diffusion models and facilitate related applications.
These weights were trained on stable diffusion 1.5.
- Accurate pose estimation: ControlNet-Openpose accurately detects keypoints and estimates body joints, providing precise pose estimation even in challenging conditions.
- Real-time performance: Thanks to its efficient architecture and voltaML optimizations, ControlNet-Openpose delivers real-time performance, making it suitable for a wide range of applications, including interactive systems and live video analysis.
- Robust to occlusions: ControlNet-Openpose is designed to handle partial occlusions, ensuring that it maintains high accuracy even when some body parts are not visible.
ControlNet-Openpose can be utilized in various applications, such as:
- Human-computer interaction (HCI)
- Virtual and augmented reality
- Animation and game development
- Fitness and sports analytics
For more detailed instructions, refer to the API documentation and resources available on Github.
Apache License 2.0