hide
Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Graphics, cs.GR
Abstract:
We propose a deep representation of appearance, i. e. the relation of color,
surface orientation, viewer position, material and illumination. Previous
approaches have used deep learning to extract classic appearance
representations relating to reflectance model parameters (e. g. Phong) or
illumination (e. g. HDR environment maps). We suggest to directly represent
appearance itself as a network we call a deep appearance map (DAM). This is a
4D generalization over 2D reflectance maps, which held the view direction
fixed. First, we show how a DAM can be learned from images or video frames and
later be used to synthesize appearance, given new surface orientations and
viewer positions. Second, we demonstrate how another network can be used to map
from an image or video frames to a DAM network to reproduce this appearance,
without using a lengthy optimization such as stochastic gradient descent
(learning-to-learn). Finally, we generalize this to an appearance
estimation-and-segmentation task, where we map from an image showing multiple
materials to multiple networks reproducing their appearance, as well as
per-pixel segmentation.
