Full process generation method of high-resolution face texture map
Most research on face texture generation focuses on low-resolution generation.To address this,the image translation was applied to the generation of high-resolution texture maps,proposing a whole-process method for generating 1024*1024 texture maps using an image translation network as the main part.This method effectively alleviated the problem of low resolution of ultraviolet texture generation,while ensuring rapid and efficient generation.In the image translation network,the convolutional neural networks served as the backbone network,combined with the statistical texture learning network(STLNet)and the normalization method of soft adaptive layer-instance normalization(Soft-AdaLIN)to form the generator.Meanwhile,multi-scale discrimination was employed to guide the generation of high-resolution texture images,and finally color conversion and Poisson fusion were performed to complete texture correction.Images were randomly extracted from the FFHQ dataset for face normalization and tested.Through a series of evaluation indexes for quantitative evaluation,qualitative and quantitative comparisons with recent relevant research methods,the advantages of this whole-process generation method in generating 1024×1024 face UV texture images were verified.
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