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

• URL: http://arxiv.org/abs/2207.06899v1
• Date: Thu, 14 Jul 2022 13:28:08 GMT
• Title: Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation
• Authors: Boming Zhao, Bangbang Yang, Zhenyang Li, Zuoyue Li, Guofeng Zhang, Jiashu Zhao, Dawei Yin, Zhaopeng Cui, Hujun Bao
• Abstract summary: We propose a factorized neural re-rendering model to produce novel views from cluttered outdoor Internet photo collections. We also propose a novel realism augmentation process to complement appearance details, which automatically propagates the texture details from a narrow captured photo to the extrapolated neural rendered image.
• Score: 50.00344639039158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications. Although photo extrapolation has been well studied, it is much more challenging to extrapolate a photo (i.e., selfie) from a narrow field of view to a wider one while maintaining a similar visual style. In this paper, we propose a factorized neural re-rendering model to produce photorealistic novel views from cluttered outdoor Internet photo collections, which enables the applications including controllable scene re-rendering, photo extrapolation and even extrapolated 3D photo generation. Specifically, we first develop a novel factorized re-rendering pipeline to handle the ambiguity in the decomposition of geometry, appearance and illumination. We also propose a composited training strategy to tackle the unexpected occlusion in Internet images. Moreover, to enhance photo-realism when extrapolating tourist photographs, we propose a novel realism augmentation process to complement appearance details, which automatically propagates the texture details from a narrow captured photo to the extrapolated neural rendered image. The experiments and photo editing examples on outdoor scenes demonstrate the superior performance of our proposed method in both photo-realism and downstream applications.

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