How Modiface utilized TensorFlow.js in production for AR makeup try on in the browser — The TensorFlow Blog

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The end product is an experience under 3 MB (compressed) that is ready before most users get their camera started. Additionally, our accuracy on various edge cases and facial expressions is greatly improved. We’ve also deployed this with more landmarks allowing us to expand the capability of our current renderings. This new structure gives us the ground to improve further with additional training without reworking the overall architecture.

TensorFlow.js not only allows client side rendering for applications that previously could only exist on powerful devices, but also comes with a host of other benefits. Without a backend to support the tool, it lets us deploy the try on easily, at a low cost across many L’Oreal brands.

It also means a user’s image is fully in their control while maintaining privacy. To try on makeup, you do not have to send your photos and videos anywhere. Not only does this make for an experience users can trust, but it is also perfectly inline with the L’Oreal brand goals of transparency for their consumers.

trust

transparency

face detection + landmark detection ぽい