The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Image quilting for texture synthesis and transfer
Abstract
We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures. Second, we extend the algorithm to perform texture transfer — rendering an object with a texture taken from a different object. More generally, we demonstrate how an image can be re-rendered in the style of a different image. The method works directly on the images and does not require 3D information.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Cited by
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Self-citationImage Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258
A Neural Algorithm of Artistic Style
Gatys, L., Ecker, A., & Bethge, M. (2016) · Journal of Vision · doi:10.1167/16.12.326 · Cited by 2,612
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“Patch-based methods such as [5, 15] operate by searching for relevant patches from the image’s non-hole regions or other source images in an iterative fashion.”
MethodGlobally and locally consistent image completion
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073659 · Cited by 2,203
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00059 · Cited by 940
Texture optimization for example-based synthesis
Kwatra, V., Essa, I., Bobick, A., & Kwatra, N. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073263 · Cited by 608
Understanding and creating art with AI: Review and outlook
Cetinic, E., & She, J. (2022) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3475799 · Cited by 579
A two-step approach to hallucinating faces: global parametric model and local nonparametric model
Liu, C., Shum, H.-Y., & Zhang, C.-S. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990475 · Cited by 361
Face swapping: automatically replacing faces in photographs
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360638 · Cited by 333
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., & Shlens, J. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.114 · Cited by 306
Hertzmann, A. (2018) · Arts · doi:10.3390/arts7020018 · Cited by 265
AI Art and its Impact on Artists
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604681 · Cited by 265
Dimitrov, A., & Golparvar-Fard, M. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.11.002 · Cited by 261
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Zhao, S., Cui, J., Sheng, Y., Dong, Y., Liang, X., Chang, E. I., & Xu, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.10428 · Cited by 224
Wang, X., Oxholm, G., Zhang, D., & Wang, Y.-F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.759 · Cited by 182
Learning from Demonstrations Through the Use of Non-rigid Registration
Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_20 · Cited by 133
Model Synthesis: A General Procedural Modeling Algorithm
Merrell, P., & Manocha, D. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.112 · Cited by 76
Berthouzoz, F., Li, W., Dontcheva, M., & Agrawala, M. (2011) · ACM Transactions on Graphics · doi:10.1145/2019627.2019639 · Cited by 37
Metadata
- Authors
- Efros, A. A., & Freeman, W. T.
- Venue
- Proceedings of the 28th annual conference on Computer graphics and interactive techniques
- Publisher
- ACM
- Published
- 2001-08
- Pages
- 341-346
- OpenAlex
- W1999360130
- Cited by
- 2,347
- References
- 1
- FWCI
- 28.09
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
