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SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting

Varun Jampani*
Huiwen Chang*
Kyle Gregory Sargent
Dominik Philemon Kaeser
Bill Freeman
Brian Curless
Ce Liu
ICCV 2021 (2021)

Abstract

Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches for single-image view synthesis combine monocular depth network along with inpainting networks resulting in compelling novel view synthesis results. A drawback of these approaches is the use of hard layering making them not suitable to model intricate appearance effects such as matting. We present SLIDE, a modular and unified system for single image 3D photography that uses simple yet effective soft layering strategy to model appearance effects. In addition, we propose a novel depth-aware training of inpainting network suitable for 3D photography task. Extensive experimental analysis on 3 different view synthesis datasets in combination with user studies on in-the-wild image collections demonstrate the superior performance of our technique in comparison to existing strong baselines.

Research Areas