Boundless: Generative Adversarial Networks for Image Extension
arXiv:1908.07007
Abstract
Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation.
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Cited by in corpus (6)
- Sketch-Guided Scenery Image Outpainting
- Boosting Image Outpainting with Semantic Layout Prediction
- In-N-Out: Towards Good Initialization for Inpainting and Outpainting
- Context-aware Padding for Semantic Segmentation
- Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning
- Bridging the Visual Gap: Wide-Range Image Blending