Name
Abstract | ||
|---|---|---|
In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative models enables an efficient end-to-end framework for image synthesis and inpainting tasks, but existing methods based on generative models donu0027t exploit the segmentation information to constrain the object shapes, which usually lead to blurry results on the boundary. To tackle this problem, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image inpainting. This leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments. Our model factorizes the image inpainting process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results. Experiments on multiple public datasets show that our approach outperforms existing methods in optimizing the image inpainting quality, and the interactive segmentation guidance provides possibilities for multi-modal predictions of image inpainting. |
Year | Venue | DocType |
|---|---|---|
2018 | BMVC | Conference |
Volume | Citations | PageRank |
abs/1805.03356 | 5 | 0.49 |
References | Authors | |
15 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhang Song | 1 | 16 | 4.41 |
| Chao Yang | 2 | 76 | 5.73 |
| Yeji Shen | 3 | 5 | 0.49 |
| Peng Wang | 4 | 385 | 106.03 |
| Qin Huang | 5 | 30 | 11.60 |
| C.-C. Jay Kuo | 6 | 8 | 2.24 |