[Machine Learning] Simple Swap: A Machine Learning Framework for Swapping Faces

[wilkistermumbi]

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Simple Swap: A Machine Learning Framework for Swapping Faces

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Simple Swap (SimSwap) is a ML framework that aims for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face. We will give an overview of the framework, how it can be applied in various use cases, and implement an example to demonstrate how it can be used.

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By the end of this tutorial, a reader will be able to:

• Be given an overview of the framework.
• How to clone the GitHub repo for the framework.
• Installing required dependencies.
• Learn how to face swap images.

Article quality

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This framework is still relatively new. It was released in 2021. Not much information is available online. In addition, the tutorials that are there only focus on giving an overview of the model but not how to implement it. This tutorial will do just that.

References

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• SimSwap: An Efficient Framework For High Fidelity Face Swapping

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[ahmadmardeni1]

Sounds like a helpful topic - let's please be sure it adds value beyond what is in any official docs and/or what is covered in other blog sites. (the articles should go beyond a basic explanation - and it is always best to reference any EngEd article and build upon it). @wilkistermumbi

Please be attentive to grammar/readability and make sure that you put your article through a thorough editing review prior to submitting it for final approval. (There are some great free tools that we reference in EngEd resources.) ANY ARTICLE SUBMITTED WITH GLARING ERRORS WILL BE IMMEDIATELY CLOSED.

Please be sure to double-check that it does not overlap with any existing EngEd articles, articles on other blog sites, or any incoming EngEd topic suggestions (if you haven't already) to avoid any potential article closure, please reference any relevant EngEd articles in yours. - Approved