ControlNet - Openpose face [TensorArt]
TLDRIn this TensorArt tutorial, the host guides viewers through the process of using OpenPose to analyze facial expressions and poses. They demonstrate how to import close-up images of faces, adjust pre-processor settings, and utilize the control net to achieve desired facial poses. The video showcases the potential of facial OpenPose in generating characters with specific poses and expressions, saving time on repeated image generations. The host also explores creating ensemble images with multiple characters by merging facial maps and adjusting settings in TensorArt. The tutorial concludes with a preview of an upcoming project involving the creation of a portrait puzzle using photo editing and TensorArt to generate a unique group image, emphasizing individuality within a harmonious composition.
Takeaways
- 🎥 The video is a tutorial on using TensorArt's ControlNet with OpenPose to analyze facial poses.
- 📈 The presenter guides viewers through adding ControlNet and selecting OpenPose for facial analysis in TensorArt's workspace.
- 🖼️ The process involves importing close-up images of faces and observing the output with facial landmarks.
- 📸 The pre-processor setting is adjusted to 'OpenPose face only' to capture facial expressions and part of the character's pose.
- 🎨 The video demonstrates rendering a soccer player's image in a cartoon style using a specific model.
- 🔍 The presenter discusses optimizing the comic-like effect by illustrating facial OpenPose for greater control over character generation.
- 🎭 The use of ControlNet with OpenPose face is shown to achieve a specific pose defined by the user.
- 🤖 AI determines the initial pose, camera framing, and face direction, but ControlNet allows for more precise control.
- 👥 The potential of creating ensemble images with multiple characters using facial OpenPose is explored.
- 🖌️ Photo editing tools like Photopia are used to modify and align facial maps for creating group images.
- 🧩 A creative approach to generating group images is introduced, involving creating a 'portrait puzzle' with individual facial poses.
- 📚 The video concludes with an invitation to subscribe for more artistic techniques and projects using TensorArt.
Q & A
What is the main topic of the tutorial in the video?
-The main topic of the tutorial is using OpenPose to analyze and understand facial poses in the context of TensorArt's ControlNet.
How does the ControlNet button in TensorArt workspace allow the user to proceed?
-The ControlNet button in TensorArt workspace allows the user to add the ControlNet, which is then followed by selecting OpenPose in the subsequent screen.
What kind of images does the user import for facial analysis?
-The user imports close-up images of soccer players captured in an iconic moment of celebration for scoring a goal.
What is the purpose of changing the pre-processor setting to 'Open Pose Face Only'?
-Changing the pre-processor setting to 'Open Pose Face Only' allows capturing not only the facial expression but also a portion of the character's pose.
How does the user ensure that the generated images have a specific facial expression and pose?
-The user ensures specific facial expressions and poses by using the ControlNet with OpenPose Face to generate a facial pose map, which is then used as a reference for image generation.
What is the role of the 'real cartoon, 3D' model in the image generation process?
-The 'real cartoon, 3D' model is used to render the soccer player's image in a cartoon style, which may vary depending on the choice of the model.
How does the user achieve a perfect depiction of a singer in portrait format?
-The user selects the aspect ratio in portrait format in the settings section to achieve a perfect depiction of a singer.
What is the significance of using the ControlNet to achieve a pose defined by the user?
-Using the ControlNet allows the user to have greater control over generating characters with specific poses, saving time by avoiding repeated image generations until a satisfying result is achieved.
How does the user create images featuring multiple characters using facial OpenPose?
-The user creates a facial map with multiple aligned faces using a photo editing tool, adjusts the dimensions to fit the allowed dimensions in TensorArt, and then uses this map as a control image to generate images with multiple characters.
What is the final step in generating a group image that represents the individuality of each member in a harmonious composition?
-The final step involves using the facial pose maps extracted from the portrait puzzle composition in TensorArt to generate the final images, piecing together the puzzle to create an overall and unique depiction of the group.
How can viewers stay updated on the artistic adventures shared in the video?
-Viewers can stay updated by subscribing to the YouTube channel where more exciting techniques and projects are shared.
Outlines
😀 Introduction to Tensor Arts Control Net and Open Pose
The video begins with a warm welcome to the channel and an introduction to Tensor Arts Control Net. The focus is on using Open Pose for facial pose analysis. The host encourages viewers to catch up on previous videos through a playlist. The tutorial starts with adding the Control Net and selecting Open Pose, specifically for facial analysis. The process involves importing a close-up image of a face and using Open Pose Face to analyze and capture facial expressions. The host demonstrates how changing the pre-processor setting can capture both facial expression and character pose. The video showcases the results, including an image with a black background and facial expression dots. The host also discusses using different models to render images in a cartoon style and the potential for optimization.
🎭 Controlling Character Poses and Expressions with Open Pose
The host explains how Open Pose can be used to control character poses and expressions more precisely. They demonstrate this by generating images of a singing girl using Tensor Art, selecting an appropriate model, and composing the prompt. The host then uses the Control Net to achieve a specific pose by downloading an image and re-loading it into the Control Net functions box with Open Pose Face. The result is a set of images with the same pose, showcasing the effectiveness of using Control Net for precise results. The host also discusses the possibility of creating ensemble images with multiple characters using facial Open Pose.
🖼️ Creating Ensemble Images with Open Pose and Photo Editing
The host outlines a process for creating ensemble images with multiple characters using Open Pose and photo editing tools like Photo Pier. They guide the viewer through modifying the facial map, saving it, and using photo editing software to create a grid of aligned faces. The host emphasizes the importance of adjusting the dimensions of the map to fit within the allowed dimensions of Tensor Art and modifying the Control Net parameter to use the edited map for generating images. The result is a quartet of singers with the same pose, demonstrating the potential of combining photo editing and Tensor Art for creating unique compositions.
🌟 Final Thoughts and Future Creative Explorations
The host concludes by highlighting the astonishing results achieved through the use of Tensor Art and photo editing tools. They express excitement about future projects that will explore creating group images using a portrait puzzle approach. The host invites viewers to subscribe to their YouTube channel for updates on artistic adventures and thanks the audience for their attention and participation in the community.
Mindmap
Keywords
💡TensorArt
💡ControlNet
💡OpenPose
💡Pre-processor
💡Facial Expressions
💡Cartoon Style
💡Control Net Functions
💡Photo Pier
💡Portrait Puzzle
💡Artificial Intelligence (AI)
💡Image Generation
Highlights
The tutorial focuses on using OpenPose to analyze and understand facial poses in the context of tensor art.
ControlNet is added to the workspace console to begin the process.
OpenPose is selected with a pre-processor setting change to 'OpenPose face only' for facial analysis.
Close-up images of soccer players celebrating goals are used as examples.
The facial expression and character's pose are captured using the new OpenPose face command.
A cartoon-style model is chosen to render the soccer player's image.
The fin effect varies depending on the model choice.
Facial OpenPose helps achieve greater control over character generation.
Tensor Art generates images of a singing girl with a specific model and prompt.
Control Net is used to achieve a defined pose by downloading and reusing an image as a pose reference.
The potential of using Control Net to obtain desired poses is demonstrated with consistent results.
Facial OpenPose is used to obtain images featuring multiple characters.
Photo Pier is used to modify the facial map for creating ensemble images.
A quartet of singers is created by aligning and merging facial maps.
Tensor Art settings are adjusted to fit the newly created map into allowed dimensions.
Higher fix parameter is activated to enhance the resolution of the final result.
Photopia is used to create a portrait puzzle for generating group images.
Facial pose maps from the portrait puzzle are used to generate final images, capturing individuality in a harmonious composition.
The project explores creative ways to achieve astonishing results with tensor art and photo editing.