The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
Open accessAbstract
We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Our approach first explicitly infers the parts of the geometry visible both in the input and novel views and then casts the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the input image. In addition to the new network structure, training with a combination of adversarial and perceptual loss results in a reduction in common artifacts of novel view synthesis such as distortions and holes, while successfully generating high frequency details and preserving visual aspects of the input image. We evaluate our approach on a wide range of synthetic and real examples. Both qualitative and quantitative results show our method achieves significantly better results compared to existing methods.
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Cites
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Context Encoders: Feature Learning by Inpainting
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An Unsupervised Learning Model for Deformable Medical Image Registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Dalca, A. V., & Guttag, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00964 · Cited by 599
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
Cross-view image synthesis using conditional GANs
Regmi, K., & Borji, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00369 · Cited by 204
Geometry-Free View Synthesis: Transformers and no 3D Priors
Rombach, R., Esser, P., & Ommer, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01409 · Cited by 75
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers
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Metadata
- Authors
- Park, E., Yang, J., Yumer, E., Ceylan, D., & Berg, A. C.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 702-711
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2598591334
- Cited by
- 279
- References
- 15
- FWCI
- 12.54
- Type
- conference paper
- Language
- en
- Open access
- green
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