1.03D Face Fitting and Attribute Classifier Debiasinghttps://mscvprojects.ri.cmu.edu/2024team11Method and Results - 3D Face Fitting and Attribute Classifier Debiasingrich600338<blockquote class="wp-embedded-content" data-secret="kfZWqUbCYD"><a href="https://mscvprojects.ri.cmu.edu/2024team11/method-and-results/">Method and Results</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2024team11/method-and-results/embed/#?secret=kfZWqUbCYD" width="600" height="338" title="“Method and Results” — 3D Face Fitting and Attribute Classifier Debiasing" data-secret="kfZWqUbCYD" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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Approach for 3D Face Fitting We take one frame at a time and run a 2D Landmark Regression model on it. This gives us the 2D landmarks over the face. This process operates at around 90 FPS. After that the detected 2D landmarks are lifted to 3D using the landmark-vertex correspondence of the FLAME model. […]https://lh7-rt.googleusercontent.com/slidesz/AGV_vUccJFwr5wWEEeGKlCnffRi-q6yBgCX_PfRjIGeRIMz_c091t7VvZ7SVqh92LFkRbKqOfQjwEFhB7EXg2bYqQmPqBfL9dQzDMSaHnbIYJtKK5hSjaY8w3S1hR43a2aW0NEMec1YY=s2048?key=srilwSe8Xh6YTexdiQtqXTki