ML Anthology

StructureFlow: Image Inpainting via Structure-Aware Appearance Flow

Yurui Ren, Xiaoming Yu, Ruonan Zhang, Thomas H. Li, Shan Liu, Ge Li
ICCV 2019
doi:10.1109/ICCV.2019.00027 /iccv/2019/ren2019iccv-structureflow/

Abstract

Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.

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Cite

Text

Ren et al. "StructureFlow: Image Inpainting via Structure-Aware Appearance Flow." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00027

Markdown

[Ren et al. "StructureFlow: Image Inpainting via Structure-Aware Appearance Flow." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/ren2019iccv-structureflow/) doi:10.1109/ICCV.2019.00027

BibTeX

@inproceedings{ren2019iccv-structureflow,
  title     = {{StructureFlow: Image Inpainting via Structure-Aware Appearance Flow}},
  author    = {Ren, Yurui and Yu, Xiaoming and Zhang, Ruonan and Li, Thomas H. and Liu, Shan and Li, Ge},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.00027},
  url       = {https://mlanthology.org/iccv/2019/ren2019iccv-structureflow/}
}