We address the problem of novel view synthesis: given an input image,
synthesizing new images of the same object or scene observed from arbitrary
viewpoints. We approach this as a learning task but, critically, instead of
learning to synthesize pixels from scratch, we learn to copy them from the
input image. Our approach exploits the observation that the visual appearance
of different views of the same instance is highly correlated, and such
correlation could be explicitly learned by training a convolutional neural
network (CNN) to predict appearance flows -- 2-D coordinate vectors specifying
which pixels in the input view could be used to reconstruct the target view.
Furthermore, the proposed framework easily generalizes to multiple input views
by learning how to optimally combine single-view predictions. We show that for
both objects and scenes, our approach is able to synthesize novel views of
higher perceptual quality than previous CNN-based techniques.

本论文研究的问题是如何从给定的输入图像中合成出相同物体或场景在任意视角下的新图像。解决方案是通过卷积神经网络学习出现象的外在特征，从而实现像素重构，同时可以扩展到多个输入视图的合成。实验结果表明，该方法在合成出高品质图像方面的性能比之前基于 CNN 的技术更为优异。