  While implicit generative models such as GANs have shown impressive results
in high quality image reconstruction and manipulation using a combination of
various losses, we consider a simpler approach leading to surprisingly strong
results. We show that texture loss alone allows the generation of perceptually
high quality images. We provide a better understanding of texture constraining
mechanism and develop a novel semantically guided texture constraining method
for further improvement. Using a recently developed perceptual metric employing
"deep features" and termed LPIPS, the method obtains state-of-the-art results.
Moreover, we show that a texture representation of those deep features better
capture the perceptual quality of an image than the original deep features.
Using texture information, off-the-shelf deep classification networks (without
training) perform as well as the best performing (tuned and calibrated) LPIPS
metrics. The code is publicly available.
