Faceswap V2

Faceswap v2 based on ReActor, takes an image of a face and replaces it with the face of your choice. ReActor acts as an intermediary, using its understanding of faces to bridge the gap between the source face and the target image, resulting in a seemingly natural face swap. This model is an improvement over the Faceswap model which is based on Roop.

Key Components of Faceswap V2

ReActor leverages pre-existing deep learning models, which are likely pre-trained on extensive datasets of faces. These models have the capability to recognize, analyze, and manipulate facial features.

Utilizing its face detection capabilities, ReActor identifies all faces in the target image, assigning a unique number to each detected face. Once the faces are identified, ReActor creates a mask around each one, effectively isolating the facial region for subsequent manipulation.

The pre-trained models are then put to work to analyze the source face image, extracting key facial features and characteristics. The extracted facial features from the source face are seamlessly blended onto the corresponding masked area in the target image.

The model takes into account factors such as lighting, pose, and perspective to create a final image that is both realistic and cohesive. Furthermore, ReActor offers options for refining the face swap result. This involves tools to correct and enhance facial details and adjust the level of detail or sharpness in the swapped face. This ensures the final result is as accurate and natural-looking as possible.

How to use Faceswap V2

1. Target face image: This is the image where you want to replace a face.

2. Source face image: This contains the face you want to swap in.

3. Face restore: Adjust the face restore parameter to control the sharpness of the final image output. You can choose between Codeformer or GFPGAN for further enhancing the face in the image output.

*Please note, this model does not support GIF or Video, therefore the target face image should be a static image only.