lightweight-openpose

Maintainer: alaradirik

Total Score

1

Last updated 5/30/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

lightweight-openpose is a PyTorch implementation of the Lightweight OpenPose model, as introduced in the research paper "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose." This model is a lightweight version of the original OpenPose model, designed to run efficiently on CPU hardware. It can be used for real-time 2D multi-person pose estimation, a task that involves identifying the location of key body joints in an image or video.

This model is similar to other pose estimation models like t2i-adapter-sdxl-openpose, vid2openpose, and magic-animate-openpose, all of which leverage the OpenPose approach for various applications. It is also related to face restoration models like gfpgan and image editing models like masactrl-stable-diffusion-v1-4.

Model inputs and outputs

The lightweight-openpose model takes in an RGB image and a desired image size, and outputs a set of keypoints representing the estimated locations of body joints for any persons present in the image.

Inputs

  • Image: The RGB input image
  • Image Size: The desired size of the input image, which must be between 128 and 1024 pixels

Outputs

  • Keypoints: The estimated locations of body joints for each person in the input image

Capabilities

The lightweight-openpose model is capable of real-time 2D multi-person pose estimation, even on CPU hardware. This makes it suitable for a variety of applications where efficient and accurate pose estimation is required, such as video analysis, human-computer interaction, and animation.

What can I use it for?

The lightweight-openpose model can be used in a variety of applications that require understanding human pose and movement, such as:

  • Video analysis: Analyze the movements of people in video footage, for applications like video surveillance, sports analysis, or dance choreography.
  • Human-computer interaction: Use pose estimation to enable natural user interfaces, such as gesture-based controls or motion tracking for gaming and virtual reality.
  • Animation and graphics: Incorporate realistic human pose and movement into animated characters or virtual environments.

Things to try

One interesting aspect of the lightweight-openpose model is its ability to run efficiently on CPU hardware, which opens up new possibilities for deployment in real-world applications. You could try using this model to build a real-time pose estimation system that runs on edge devices or embedded systems, enabling new use cases in areas like robotics, autonomous vehicles, or industrial automation.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

AI model preview image

lightweight-openpose

adirik

Total Score

4

The lightweight-openpose model is a PyTorch implementation of the Lightweight OpenPose algorithm, as introduced in the research paper "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose". This model is designed to perform real-time 2D multi-person pose estimation on a CPU, making it a lightweight and efficient solution for various computer vision applications. Compared to similar models like t2i-adapter-sdxl-openpose and t2i-adapter-sdxl-openpose, the lightweight-openpose model focuses specifically on human pose estimation rather than image manipulation. Model inputs and outputs The lightweight-openpose model takes an RGB input image and an image size parameter as inputs. The image size can be set between 128 and 1024 pixels, with a default of 256 pixels. The model outputs a set of keypoints representing the estimated poses of people in the input image. Inputs image**: The input RGB image image_size**: The size of the input image, ranging from 128 to 1024 pixels (default is 256) show_visualisation**: An optional boolean flag to draw and visualize the keypoints on the input image Outputs The output of the model is a set of keypoints representing the estimated poses of people in the input image. Capabilities The lightweight-openpose model is capable of estimating the 2D poses of multiple people in a single input image. It can detect and locate the key joints and body parts of people, such as the head, shoulders, elbows, wrists, hips, knees, and ankles. This information can be useful for a variety of computer vision tasks, such as human-computer interaction, sports analytics, and video surveillance. What can I use it for? The lightweight-openpose model can be used in a wide range of applications that involve human pose estimation, such as: Human-computer interaction**: Tracking user movements and gestures for natural user interfaces, virtual reality, and augmented reality applications. Sports analytics**: Analyzing the form and technique of athletes during training or competition to provide feedback and improve performance. Video surveillance**: Monitoring and analyzing human behavior in security and public safety applications. Animation and film production**: Capturing and animating human movements for digital characters and special effects. Things to try One interesting thing to try with the lightweight-openpose model is to combine it with other computer vision or image processing techniques, such as stable-diffusion. By using the pose information extracted by lightweight-openpose, you could potentially manipulate or generate images in creative ways that incorporate the human form and movement.

Read more

Updated Invalid Date

AI model preview image

t2i-adapter-sdxl-openpose

alaradirik

Total Score

56

The t2i-adapter-sdxl-openpose model is a text-to-image diffusion model that enables users to modify images using human pose information. This model is an implementation of the T2I-Adapter-SDXL model, which was developed by TencentARC and the diffuser team. It allows users to generate images based on a text prompt and control the output using an input image's human pose. This model is similar to other text-to-image models like t2i-adapter-sdxl-lineart, which uses line art instead of pose information, and masactrl-sdxl, which provides more general image editing capabilities. It is also related to models like vid2openpose and magic-animate-openpose, which work with OpenPose input. Model inputs and outputs The t2i-adapter-sdxl-openpose model takes two primary inputs: an image and a text prompt. The image is used to provide the human pose information that will be used to control the generated output, while the text prompt specifies the desired content of the image. Inputs Image**: The input image that will be used to provide the human pose information. Prompt**: The text prompt that describes the desired output image. Outputs Generated Images**: The model outputs one or more generated images based on the input prompt and the human pose information from the input image. Capabilities The t2i-adapter-sdxl-openpose model allows users to generate images based on a text prompt while incorporating the human pose information from an input image. This can be useful for tasks like creating illustrations or digital art where the pose of the subjects is an important element. What can I use it for? The t2i-adapter-sdxl-openpose model could be used for a variety of creative projects, such as: Generating illustrations or digital art with specific human poses Creating concept art or character designs for games, films, or other media Experimenting with different poses and compositions in digital art The ability to control the human pose in the generated images could also be valuable for applications like animation, where the model's output could be used as a starting point for further refinement. Things to try One interesting aspect of the t2i-adapter-sdxl-openpose model is the ability to use different input images to influence the generated output. By providing different poses, users can experiment with how the human figure is represented in the final image. Additionally, users could try combining the pose information with different text prompts to see how the model responds and generates new variations.

Read more

Updated Invalid Date

AI model preview image

t2i-adapter-sdxl-openpose

adirik

Total Score

73

The t2i-adapter-sdxl-openpose model is a text-to-image generation model that allows users to modify images using human pose. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the diffuser team. The model is available through Replicate and can be accessed using the Cog interface. Similar models created by the same maintainer, adirik, include the t2i-adapter-sdxl-sketch model for modifying images using sketches, and the t2i-adapter-sdxl-lineart model for modifying images using line art. The maintainer has also created the t2i-adapter-sdxl-sketch model with a different creator, alaradirik, as well as the t2i-adapter-sdxl-depth-midas model for modifying images using depth maps. Model inputs and outputs The t2i-adapter-sdxl-openpose model takes in an input image, a prompt, and various optional parameters such as the number of samples, guidance scale, and number of inference steps. The output is an array of generated images based on the input prompt and the modifications made using the human pose. Inputs Image**: The input image to be modified. Prompt**: The text prompt describing the desired output. Scheduler**: The scheduler to use for the diffusion process. Num Samples**: The number of output images to generate. Random Seed**: A random seed for reproducibility. Guidance Scale**: The guidance scale to match the prompt. Negative Prompt**: Specifies things to not see in the output. Num Inference Steps**: The number of diffusion steps. Adapter Conditioning Scale**: The conditioning scale for the adapter. Adapter Conditioning Factor**: The factor to scale the image by. Outputs An array of generated images based on the input prompt and human pose modifications. Capabilities The t2i-adapter-sdxl-openpose model can be used to modify images by incorporating human pose information. This allows users to generate images that adhere to specific poses or body movements, opening up new creative possibilities for visual art and content creation. What can I use it for? The t2i-adapter-sdxl-openpose model can be used for a variety of applications, such as creating dynamic and expressive character illustrations, generating poses for animation or 3D modeling, and enhancing visual storytelling by incorporating human movement into the generated imagery. With the ability to fine-tune the model's parameters, users can explore a range of creative directions and experiment with different styles and aesthetics. Things to try One interesting aspect of the t2i-adapter-sdxl-openpose model is the ability to combine the human pose information with other modification techniques, such as sketches or line art. By leveraging the different adapters created by the maintainer, users can explore unique blends of visual elements and push the boundaries of what's possible with text-to-image generation.

Read more

Updated Invalid Date

AI model preview image

realistic-vision-v5-openpose

lucataco

Total Score

5

The realistic-vision-v5-openpose model is an implementation of the SG161222/Realistic_Vision_V5.0_noVAE model with OpenPose, created by lucataco. This model aims to generate realistic images based on a text prompt, while incorporating OpenPose to leverage pose information. It is similar to other Realistic Vision models developed by lucataco, each with its own unique capabilities and use cases. Model inputs and outputs The realistic-vision-v5-openpose model takes a text prompt, an input image, and various configurable parameters as inputs. The text prompt describes the desired output image, while the input image provides pose information to guide the generation. The model outputs a generated image that matches the given prompt and leverages the pose information from the input. Inputs Image**: The input pose image to guide the generation Prompt**: The text description of the desired output image Seed**: The random seed value (0 for random, up to 2147483647) Steps**: The number of inference steps (0-100) Width**: The desired width of the output image (0-1920) Height**: The desired height of the output image (0-1920) Guidance**: The guidance scale (3.5-7) Scheduler**: The scheduler algorithm to use for inference Outputs Output image**: The generated image that matches the input prompt and leverages the pose information Capabilities The realistic-vision-v5-openpose model is capable of generating highly realistic images based on text prompts, while incorporating pose information from an input image. This allows for the creation of visually striking and anatomically accurate portraits, scenes, and other content. The model's attention to detail and ability to capture the nuances of human form and expression make it a powerful tool for a variety of applications, from art and design to visual storytelling and beyond. What can I use it for? The realistic-vision-v5-openpose model can be used for a wide range of creative and professional applications. Artists and designers can leverage the model to generate unique, high-quality images for use in illustrations, concept art, and other visual media. Content creators can use the model to enhance their video productions, adding realistic character animations and poses to their scenes. Researchers and developers can explore the model's capabilities for applications in areas like virtual reality, augmented reality, and human-computer interaction. Things to try One interesting aspect of the realistic-vision-v5-openpose model is its ability to generate images that seamlessly combine realistic elements with more abstract or stylized components. By experimenting with different input prompts and pose images, users can explore the model's capacity to blend realism and imagination, creating visually striking and emotionally evocative artworks. Additionally, users may want to try varying the model's configuration parameters, such as the guidance scale or the scheduler, to observe the impact on the generated output and discover new creative possibilities.

Read more

Updated Invalid Date