In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes.
We compare against a modular single image view synthesis approach. 3D Moments warps the highlights along with the texture.
@article{Paliwal2023reshader,
author = {Paliwal, Avinash and Nguyen, Brandon G. and Tsarov, Andrii and Kalantari, Nima Khademi},
title = {ReShader: View-Dependent Highlights for Single Image View-Synthesis},
journal = {ACM Trans. Graph.},
publisher = {Association for Computing Machinery},
year = {2023},
issue_date = {December 2023},
volume = {42},
number = {6},
articleno = {216},
numpages = {9},
month = {dec},
doi = {10.1145/3618393},
}
We thank the SIGGRAPH Asia reviewers for their comments and suggestions. This work was funded by Leia Inc. (contract #415290). Nima Khademi Kalantari was in part supported by CAREER Award (#2238193). Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.