Maximov, M;
Leal-Taixe, L;
Fritz, M;
Ritschel, T;
(2019)
Deep Appearance Maps.
The IEEE International Conference on Computer Vision (ICCV)
pp. 8729-8738.
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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 show the example of an appearance estimation-and-segmentation task, mapping from an image showing multiple materials to multiple deep appearance maps.
Type: | Article |
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Title: | Deep Appearance Maps |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://openaccess.thecvf.com/content_ICCV_2019/htm... |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10085050 |
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