Ivan Neulander
I'm on the Daydream team. Prior to that, my background was in photorealistic computer rendering.
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DeepStereo: Learning to Predict New Views From the World's Imagery
John Flynn
James Philbin
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
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Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [22, 32], but their use in graphics problems has been limited ([23, 7] are notable recent exceptions). In this work, we present a novel deep architecture that per- forms new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to tradi- tional approaches which consist of multiple complex stages of processing, each of which require careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network which then directly produces the pixels of the unseen view. The benefits of our approach include gen- erality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system which is able to plausibly generate pixels according to color, depth, and tex- ture priors learnt automatically from the training data. We show view interpolation results on imagery from the KITTI dataset [12], from data from [1] as well as on StreetView images. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.
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We discuss the innovative fur rendering technology that Rhythm & Hues deployed in the photorealistic depiction of the tiger Richard Parker for Ang Lee's Academy-Award-winning feature film "Life of Pi".
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