Supplemental Material for Photorealistic Facial Texture Inference Using Deep Neural Networks
نویسندگان
چکیده
Our main results in the paper demonstrate successful inference of high-fidelity texture maps from unconstrained images. The input images have mostly low resolutions, nonfrontal faces, and the subjects are often captured in challenging lighting conditions. We provide additional results with pictures from the annotated faces-in-the-wild (AFW) dataset [10] to further demonstrate how photorealistic porelevel details can be synthesized using our deep learning approach. We visualize in Figure 9 the input, the intermediate low-frequency albedo map obtained using a linear PCA model, and the synthesized high-frequency albedo texture map. We also show several views of the final renderings using the Arnold renderer [13]. We refer to the accompanying video for additional rotating views of the resulting textured 3D face models.
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