Controlnet Pose


AI model preview image
The controlnet-pose model is an image-to-image translation model trained in a supervised manner using the ControlNet dataset. It is specifically designed to modify images that contain humans by applying pose detection techniques. The model provides the ability to manipulate images with human subjects by detecting and altering their poses, allowing for various creative and practical applications.

Use cases

The controlnet-pose model has several potential use cases. It can be used in creative applications such as generating realistic animations or enhancing visual effects in movies and video games. For example, it can be used to modify the poses of characters in a game or to animate a 3D model based on the pose of a human subject in an image. In the field of computer vision, this model can aid in pose estimation by refining or generating more accurate pose labels for training datasets. It can also be used to generate synthetic images with annotated poses for tasks such as human action recognition or pose estimation. The controlnet-pose model can also find practical applications in fields like fashion and e-commerce. It can be used to create virtual try-on experiences, where users can see how clothes or accessories would look on them based on their own poses. For online retailers, it can help generate personalized product recommendations by analyzing a user's pose and suggesting items that would complement their body shape and posture. In the healthcare industry, this model can be used for rehabilitation purposes. It can track and analyze a patient's movements and poses during therapy sessions, providing valuable feedback to both the patient and the therapist. The model can also assist in designing personalized exercise routines by analyzing a user's poses and suggesting appropriate exercises based on their individual needs and capabilities. Overall, the controlnet-pose model opens up a range of possibilities for creative expression, computer vision research, virtual try-on experiences, personalized recommendations, and healthcare applications. Its ability to detect and manipulate human poses in images provides a powerful tool for both artistic and practical purposes.



Cost per run
Avg run time
Nvidia A100 (40GB) GPU

Creator Models

Controlnet Hough$0.02078,447,391
Controlnet Seg$0.0368123,507
Stable Diffusion Depth2img$0.032250,528
Controlnet Normal$0.0368262,272

Similar Models

Try it!

You can use this area to play around with demo applications that incorporate the Controlnet Pose model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.

Currently, there are no demos available for this model.


Summary of this model and related resources.

Model NameControlnet Pose
Modify images with humans using pose detection
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.0391
Prediction HardwareNvidia A100 (40GB) GPU
Average Completion Time17 seconds