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- W4385694313 abstract "Previewing the shaded output of 3D models has been a long-standing requirement in the field. Typically, this is achieved by applying common materials; however, this approach is often labor-intensive and can yield only rough results in the trial stage. Conventional 2D style transfer methods are unsuitable for 3D-to-2D cross-domain conversion, and they cannot accurately reflect the mesh’s geometry. Inspired by StyleGAN2’s related research, we propose a method for rendering 2D images of 3D face meshes directly controlled by a single 2D reference image, using GAN disentanglement. Our approach involves an input of a 3D mesh and a reference image, where encoders extract geometric features from the mesh and appearance features from the reference image. These features control the StyleGAN2 generator to obtain a generated image that preserves the 3D mesh’s geometry and the reference image’s appearance. Our experiments demonstrate that this method performs well in generating images while maintaining geometric consistency." @default.
- W4385694313 created "2023-08-10" @default.
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- W4385694313 date "2023-08-01" @default.
- W4385694313 modified "2023-09-27" @default.
- W4385694313 title "Neural 3D face rendering conditioned on 2D appearance via GAN disentanglement method" @default.
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- W4385694313 doi "https://doi.org/10.1016/j.cag.2023.08.008" @default.
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