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

• URL: http://arxiv.org/abs/2007.06620v2
• Date: Thu, 27 Aug 2020 22:18:24 GMT
• Title: AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
• Authors: Xiaofeng Liu, Tong Che, Yiqun Lu, Chao Yang, Site Li, Jane You
• Abstract summary: This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. We construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction.
• Score: 27.163052958878776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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