The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
View Synthesis by Appearance Flow
Open accessAbstract
We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints. We approach this as a learning task but, critically, instead of learning to synthesize pixels from scratch, we learn to copy them from the input image. Our approach exploits the observation that the visual appearance of different views of the same instance is highly correlated, and such correlation could be explicitly learned by training a convolutional neural network (CNN) to predict appearance flows – 2-D coordinate vectors specifying which pixels in the input view could be used to reconstruct the target view. Furthermore, the proposed framework easily generalizes to multiple input views by learning how to optimally combine single-view predictions. We show that for both objects and scenes, our approach is able to synthesize novel views of higher perceptual quality than previous CNN-based techniques.
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Cites
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Cited by
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Kerbl, B., Kopanas, G., Leimkuehler, T., & Drettakis, G. (2023) · ACM Transactions on Graphics · doi:10.1145/3592433 · Cited by 6,025
“Deep learning techniques were adopted early for novel-view synthesis [Flynn et al. 2016; Zhou et al. 2016]; CNNs were used to estimate blendingweights [Hedman et al.”
MethodUnsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
Self-citationTaming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2020) · arXiv · doi:10.48550/arxiv.2012.09841 · Cited by 2,514
Metadata
- Authors
- Zhou, T., Tulsiani, S., Sun, W., Malik, J., & Efros, A. A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 286-301
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2348664362
- Cited by
- 579
- References
- 37
- FWCI
- 22.67
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhou, T., Tulsiani, S., Sun, W., Malik, J., & Efros, A. A. (2016). View Synthesis by Appearance Flow. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46493-0_18
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