[Source Image] ----+ |---> [Dense Motion Network] ---> [Generator Network] ---> [Animated Output] [Driving Video] ---+ ^ | [Keypoint Detector] 1. The Keypoint Detector
vox-adv-cpk.pth.tar vs vox-cpk.pth.tar #35 - alievk - GitHub
. It allows the software to animate a static image of a face (the "avatar") using the real-time facial movements of a user captured via webcam. Core Function and Architecture Model Origin : This checkpoint belongs to the First Order Motion Model for Image Animation
It identifies key landmarks (eyes, nose, mouth) in both the source image and the driving video. Vox-adv-cpk.pth.tar
Because of the file size (often several hundred megabytes), developers usually host this file on cloud storage networks like Google Drive, Yandex Disk, or Hugging Face. You will typically download it directly via your terminal using gdown or wget into your project's checkpoints/ directory. Step 2: Code Integration
Are you executing this on a or an online platform like Google Colab ?
In machine learning, a checkpoint is a snapshot of the model’s weights and parameters at a specific point during training. Instead of training a model from scratch for weeks, you load a checkpoint to instantly utilize its pre-learned capabilities. [Source Image] ----+ |---> [Dense Motion Network] --->
This file is famously associated with the First Order Motion Model for Image Animation repository, widely used in projects like Avatarify for real-time face replacement. How It Works: First-Order Motion Model
The model intelligently "paints in" parts of the face that disappear or appear during movement, such as the inside of a mouth when a person opens it or the skin behind turning head angles. Common Use Cases and Applications
The First Order Motion Model changed the game by introducing a framework that requires . Instead, it uses a self-supervised approach to learn how objects move. The model automatically detects "keypoints" along with their local affine transformations, allowing it to animate faces, bodies, and even clothing seamlessly. Core Function and Architecture Model Origin : This
python demo.py \ --config config/vox-256.yaml \ --driving_video path/to/driving/video.mp4 \ --source_image path/to/source/image.png \ --checkpoint vox-adv-cpk.pth.tar \ --relative \ --adapt_scale
python demo.py \ --config config/vox-256.yaml \ --driving_video path/to/driving.mp4 \ --source_image path/to/source.png \ --checkpoint path/to/Vox-adv-cpk.pth.tar \ --result_video path/to/output.mp4 \ --cpu Use code with caution.
If you have ever experimented with deepfake technology, automated animation, or real-time motion transfer, you have likely encountered this file. It serves as a foundational pre-trained model checkpoint that powers some of the most popular open-source image animation frameworks in existence.
Understanding Vox-adv-cpk.pth.tar: The Core Blueprint of AI Motion Transfer
This indicates the dataset used to train the model. VoxCeleb is a large-scale audiovisual dataset containing hundreds of thousands of utterances from thousands of celebrities, extracted from YouTube videos. This means the model is exceptionally good at recognizing, tracking, and reconstructing human faces and expressions.
