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This paper deals with the challenging task of synthesizing novel views forin-the-wild photographs. Existing methods have shown promising resultsleveraging monocular depth estimation and color inpainting with layered depthrepresentations. However, these methods still have limited capability to handlescenes with complex 3D geometry. We propose a new method based on themultiplane image (MPI) representation. To accommodate diverse scene layouts inthe wild and tackle the difficulty in producing high-dimensional MPI contents,we design a network structure that consists of two novel modules, one for planedepth adjustment and another for depth-aware color prediction. The formeradjusts the initial plane positions using the RGBD context feature and anattention mechanism. Given adjusted depth values, the latter predicts the colorand density for each plane separately with proper inter-plane interactionsachieved via a feature masking strategy. To train our method, we constructlarge-scale stereo training data using only unconstrained single-view imagecollections by a simple yet effective warp-back strategy. The experiments onboth synthetic and real datasets demonstrate that our trained model worksremarkably well and achieves state-of-the-art results.

Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images