CanonSwap: A tool for realizing high-fidelity face-swapping in video

CanonSwap is a research project and framework focusing on video face-swapping technology. It focuses on solving a core challenge in existing technologies: when replacing the face of a character in a video, it often destroys the dynamic attributes of the character in the original video, such as expression, head movement, and mouth synchronization, resulting in an unnatural and unstable face-swap effect.

To solve this problem, CanonSwap has come up with an innovative approach. It first transforms each frame of the video into a so-called "Canonical Space". In this special space, information about a face's appearance and motion (e.g., expression, gesture) are separated from each other. In this way, researchers can modify only the appearance information without affecting the original movements and expressions. After the face replacement, the images are "invited back" from this canonical space to the original video, and their original motion information is given back.

In this way, CanonSwap is able to generate face-swapping effects that are of high visual quality, retain identity information intact, and move consistently without flickering during video playback. The project also designed a module called "Local Identity Modulation (PIM)" that more accurately blends the features of the new face into the target facial region, thus reducing image distortion and unnecessary modifications.

Function List

• High-quality identity migration: The ability to migrate a face in an image (source) to a face in a video (target) with high fidelity while reducing image distortion and artifacts.
• timing consistency: The transition between frames of the generated face-swapped video is smooth and natural, effectively avoiding the flickering and jittering problems commonly found in traditional methods.
• Dynamic attribute retentionThe original head posture, facial expression, mouth synchronization and other dynamic features of the characters in the target video are completely retained, which makes the face-swapping effect more realistic.
• Decoupling motion and appearanceThe core technology to realize high-quality face transplantation is the separation of facial appearance and motion information through an innovative "normative space" transformation framework.
• Local Identity Modulation (PIM): A specially designed module that accurately recognizes and modifies only facial areas, avoiding unwanted effects on non-facial areas of the video.
• Facial animation generation: In addition to face-swapping, the framework also supports facial animation functionality, which enables the driving of static faces by applying expressions and movements from the source image to the target image.

Using Help

CanonSwap is a deep learning-based video face-swapping framework, not a software with a graphical user interface, which cannot be directly downloaded and installed for use by ordinary users. Its use is mainly oriented to researchers or developers with programming and artificial intelligence background, by configuring the environment and running the code to realize the video face-swap.

The following hypothetical usage help has been organized based on its technical principles and general AI project flow, and is intended to help understand its workflow:

Step 1: Environmental preparation

As an AI project, running CanonSwap requires a computer configured with a deep learning environment.

1. software: A good NVIDIA graphics card (GPU) is required because deep learning models are very computationally intensive.
2. hardware::
   • Operating system: usually Linux (e.g. Ubuntu).
   • Programming language: Python 3.x.
   • Deep learning frameworks: PyTorch or TensorFlow, etc. need to be installed.
   • Other dependent libraries: A range of Python libraries such as OpenCV (for image and video processing), NumPy (for scientific computing), etc. need to be installed. Often projects will provide arequirements.txtfile, you can use the commandpip install -r requirements.txtto install all the necessary libraries in one click.

Step 2: Obtain project documents

1. Developers need to download from the project's code hosting platform (e.g., GitHub) theCanonSwapThe source code of the
2. It is also necessary to download the project's trained model files (Pre-trained Models). These files, which have been trained with a large amount of data, contain the core data for realizing the face-swapping ability and are usually large in size.

Step 3: Prepare the input material

1. Source Image: A clear picture of a face that you wish to swap into a video.
2. Target Video: A video in which faces will be replaced.

Step 4: Execute the face change operation (core process)

Developers run CanonSwap's scripts through a command line tool that automatically performs the following complex technical processes behind the scenes:

1. startup script: In the terminal (command line interface), enter a command similar to the following to start the face change program:

   python run_inference.py --source_image path/to/source_face.jpg --target_video path/to/target_video.mp4 --output_video path/to/result.mp4
   ```2.  **身份特征提取**：程序首先会运行一个“身份编码器”（ID encoder），从你提供的`源图片`中提取出核心的面部身份特征。
   

2. Access to normative space::
   • Next, the program goes frame by frame目标视频The
   • A Motion Extractor analyzes each frame for motion information such as head pose and expression.
   • Based on this motion information, the program "warps" or "transforms" each frame into a standardized pose, which is called "normative space". This state is called "normative space". In this space, all frames have the human face facing forward, with no change in expression.
3. Perform face-switching (localized identity modulation)::
   • In the canonical space, the identity features previously extracted from the source image are accurately fused to the facial region of the target frame by the "Local Identity Modulation (PIM)" module.
   • The PIM module generates a spatial mask that ensures that modifications are made only to key areas such as the eyes, nose, and mouth, while areas such as the background and hair remain unchanged.
4. Return to the original space::
   • The canonical spatial image of the changed face is "reverse warped" back to its original pose and expression using the motion information recorded in step 3.
   • This process ensures that the character's movements and expressions are exactly the same as the original video after the new face is put on.
5. Generate results: All processed frames are recomposed into a new video file (e.g.result.mp4), and this is the final result of the face swap.

Through this series of automated steps, theCanonSwapThe complex task of preserving the video's native dynamics while accomplishing high-quality identity replacement is ultimately realized.

application scenario

1. post-production for film and television
   In movie or TV production, it can be used to change the face of a stuntman or to make up shots when the actor is unavailable. By preserving the dynamics and expressions of the original performance, the cost and difficulty of post-production can be significantly reduced.
2. AI avatars and digital content creation
   Creators can use this technology to give any human face vivid expressions and movements for virtual anchors, digital customer service or gaming characters, allowing for a more natural and vivid representation of virtual characters.
3. Education and training
   It can be used to create instructional presentation videos, such as applying the faces of historical figures to actors to create more immersive history teaching content.
4. Entertainment & Social Media
   Users can create fun short videos on social media, such as swapping their own or a friend's face into a classic movie clip or a celebrity, while retaining the original video's great performances.

QA

1. Is CanonSwap software for the average person?
   No. CanonSwap is a technical framework aimed at researchers and developers with a background in AI and programming. It does not provide GUI software that can be used directly by the average user, and needs to be operated through code and the command line.
2. How is this technology different from the common face-swapping apps on the market?
   Face-swapping apps on the market usually focus more on entertainment and ease of use, and may lose some details or produce jitter when processing videos, while CanonSwap is an academic research project focusing on solving the core technical problems, and its main goal is to achieve "high fidelity" and "temporal stability", i.e., to preserve identity features while fully synchronizing the dynamic attributes of the original video (e.g., expressions, mouths, gestures) so that the results can achieve film-grade stability and realism. The main goal of CanonSwap is to achieve "high fidelity" and "temporal stability", i.e., while preserving the identity features, it fully synchronizes the dynamic attributes of the original video (e.g., facial expressions, mouth shapes, and gestures), so that the result of face-swapping can achieve movie and TV level stability and realism.
3. Is it fake to use CanonSwap for face swapping?
   According to its research paper, CanonSwap is significantly better than many existing methods. By decoupling motion and appearance information, CanonSwap specifically solves the problems of "stiff face", mismatched expressions and flickering in face-swap videos, and produces videos with excellent visual quality and consistency.
4. What kind of facial animation can CanonSwap do?
   In addition to swapping A's face onto B's video, CanonSwap can also apply A's expressions and movements to B's face. This means that you can use a video to drive a static picture and make the people in the picture move the way they do in the video, which has great potential for application in scenarios such as avatar drives.