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arXiv · 2007.06620

AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation

Xiaofeng Liu·Tong Che·Yiqun Lu·Chao Yang·Site Li·Jane You
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Abstract

This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several appearance-describing images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.

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Xiaofeng Liu, Tong Che, Yiqun Lu, Chao Yang, Site Li, Jane You. 2020-07-13. AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation. https://doi.org/10.1007/978-3-030-58545-7_4

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