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ACCURATELY GENERATING VIRTUAL TRY-ON IMAGES UTILIZING A UNIFIED NEURAL NETWORK FRAMEWORK

机译:利用统一的神经网络框架准确地生成虚拟试样图像

摘要

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.
机译:本公开涉及用于利用统一的神经网络框架生成虚拟尝试数字图像的系统,方法和非暂时性计算机可读介质。例如,所公开的系统可以利用粗略扭曲过程来生成产品数字图像的翘曲版本以适合模型数字图像。另外,所公开的系统可以利用纹理传输过程来生成校正的分割掩模,指示模型数字图像的部分以用扭曲的产品数字图像替换。所公开的系统可以进一步基于翘曲的产品数字图像,模型数字图像和校正的分割掩模生成虚拟试验数字图像。在一些实施例中,所公开的系统可以训练一个或多个神经网络以产生用于生成虚拟试样数字图像的各个阶段的准确输出。

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