Live_Portrait_Monitor
LivePortraitMonitor is an open-source project for animating portraits using a monitor or webcam, based on deep learning techniques for researchers and developers.
English
English
What is LivePortraitMonitor?
LivePortraitMonitor is an open-source project that animates portraits using monitors or network cameras. Based on the LivePortrait research paper, it uses deep learning techniques such as image stitching and redirection control to achieve portrait animation efficiently. This project is actively being updated and improved for research purposes.
Target Audience:
Researchers and developers interested in deep learning, computer vision, and image processing can benefit from this project. It offers new ways to explore facial expression capture and animation, which can be useful for creating interactive and expressive content.
Usage Scenarios:
Researchers can use LivePortraitMonitor for real-time facial expression capture and animation.
Developers can integrate this into their applications to offer users customizable portrait animations.
Educational institutions can use this technology to teach students about deep learning and image animation.
Key Features:
Clone the repository and set up the environment by creating a Python environment with conda and installing dependencies.
Download pre-trained weights and the InsightFace face detection model.
Run inference scripts like inferencemonitor.py or inferenceorg.py to generate animated videos.
Customize input images and driving videos through command-line parameters.
Use the speed.py script to evaluate the speed of each module's inference.
Acknowledges contributions from projects like FOMM, Open Facevid2vid, SPADE, and InsightFace.
Getting Started:
1. Clone the LivePortraitMonitor project to your local machine.
2. Create and activate a Python 3.9.18 environment using conda.
3. Install the required dependencies via pip.
4. Download and extract the pre-trained weights and InsightFace face detection model to the specified directory.
5. Run the inferencemonitor.py or inferenceorg.py script to generate animated videos.
6. Adjust command-line parameters as needed, such as input image paths and driving video paths.
7. Use the speed.py script to evaluate the speed of the inference process.
