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Expanding an existing tourist photo from a partially captured scene to a fullscene is one of the desired experiences for photography applications. Althoughphoto extrapolation has been well studied, it is much more challenging toextrapolate a photo (i.e., selfie) from a narrow field of view to a wider onewhile maintaining a similar visual style. In this paper, we propose afactorized neural re-rendering model to produce photorealistic novel views fromcluttered outdoor Internet photo collections, which enables the applicationsincluding controllable scene re-rendering, photo extrapolation and evenextrapolated 3D photo generation. Specifically, we first develop a novelfactorized re-rendering pipeline to handle the ambiguity in the decompositionof geometry, appearance and illumination. We also propose a composited trainingstrategy to tackle the unexpected occlusion in Internet images. Moreover, toenhance photo-realism when extrapolating tourist photographs, we propose anovel realism augmentation process to complement appearance details, whichautomatically propagates the texture details from a narrow captured photo tothe extrapolated neural rendered image. The experiments and photo editingexamples on outdoor scenes demonstrate the superior performance of our proposedmethod in both photo-realism and downstream applications.

Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation