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Using machine learning in the browser to lip sync to your favorite songs

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blog.tensorflow.org
Posted by Pohung Chen, Creative Technologist, Google Partner InnovationToday we are releasing to let you lip sync to provides a real-time high density estimate of key points of your facial expression using only a webcam and on device machine learning - meaning no data ever leaves your machine for inference. We essentially use the key points around the mouth and lips to estimate how well you synchronize to the lyrics of the Dance Monkey song.

Determining Correctness

When first testing the demo, many people assumed we used a complex lip reading algorithm to match the mouth shapes with lyrics. Lip reading is quite difficult to achieve, so we came up with a simpler solution. We capture a frame by frame recording of the “correct” mouth shapes lined up with the music, and then when the user is playing the game, we compare the mouth shapes to the pre-recorded baseline.
While this method mostly works, there were still edge cases that made the detection algorithm not as robust, so we explored another method called Hu Moments explained below.

OpenCV matchShapes Hu Moments

In the OpenCV library, there is a matchShapes function which compares contours and returns a similarity score. Underneath the hood, the matchShapes function uses a technique called Vocal Track
enables web-based, playful, interactive experiences that go beyond basic face filters, and with a little bit of creative thinking, we could get a lip sync experience without needing the full complexity of a full lip reading ML model.

So go ahead and .

We would also like to give a special shout out to Kiattiyot Panichprecha, Bryan Tanaka, KC Chung, Dave Bowman, Matty Burton, Roger Chang, Ann Yuan, Sandeep Gupta, Miguel de Andrés-Clavera, Alessandra Donati, and Ethan Converse for their help in bringing this experience to life, and to thank the MediaPipe team who designed Facemesh.
Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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