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Computer Science, Graphics, cs.GR,Computer Science, Learning, cs.LG
Abstract:
In computer vision, Convolutional Neural Networks (CNNs) have recently
achieved new levels of performance for several inverse problems where RGB pixel
appearance is mapped to attributes such as positions, normals or reflectance.
In computer graphics, screen-space shading has recently increased the visual
quality in interactive image synthesis, where per-pixel attributes such as
positions, normals or reflectance of a virtual 3D scene are converted into RGB
pixel appearance, enabling effects like ambient occlusion, indirect light,
scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we
consider the diagonal problem: synthesizing appearance from given per-pixel
attributes using a CNN. The resulting Deep Shading simulates all screen-space
effects as well as arbitrary combinations thereof at competitive quality and
speed while not being programmed by human experts but learned from example
images.
