https://www.selleckchem.com/products/VX-745.html
To enable operation "in-the-wild", we use a robust objective function, namely Gradient Correlation. Our approach performs comparably with deep learning (DL) methods on "in-the-wild" data that have inexact ground truth, and better than DL methods on more controlled data with exact ground truth. Since our formulation does not require any learning, it enjoys a versatility that allows it to operate with multiple frames of arbitrary sizes. This study's results encourage further research on 3DMM fitting with inequality-constrained optimization
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