Very Long Natural Scenery Image Prediction by Outpainting

• URL: http://arxiv.org/abs/1912.12688v1
• Date: Sun, 29 Dec 2019 16:29:01 GMT
• Title: Very Long Natural Scenery Image Prediction by Outpainting
• Authors: Zongxin Yang, Jian Dong, Ping Liu, Yi Yang, Shuicheng Yan
• Abstract summary: Outpainting receives less attention due to two challenges in it. First challenge is how to keep the spatial and content consistency between generated images and original input. Second challenge is how to maintain high quality in generated results.
• Score: 96.8509015981031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input. To solve the two problems, we devise some innovative modules, named Skip Horizontal Connection and Recurrent Content Transfer, and integrate them into our designed encoder-decoder structure. By this design, our network can generate highly realistic outpainting prediction effectively and efficiently. Other than that, our method can generate new images with very long sizes while keeping the same style and semantic content as the given input. To test the effectiveness of the proposed architecture, we collect a new scenery dataset with diverse, complicated natural scenes. The experimental results on this dataset have demonstrated the efficacy of our proposed network. The code and dataset are available from https://github.com/z-x-yang/NS-Outpainting.

Related papers

• T-former: An Efficient Transformer for Image Inpainting [50.43302925662507]
  A class of attention-based network architectures, called transformer, has shown significant performance on natural language processing fields. In this paper, we design a novel attention linearly related to the resolution according to Taylor expansion, and based on this attention, a network called $T$-former is designed for image inpainting. Experiments on several benchmark datasets demonstrate that our proposed method achieves state-of-the-art accuracy while maintaining a relatively low number of parameters and computational complexity.
  arXiv  Detail & Related papers  (2023-05-12T04:10:42Z)
• A Wasserstein GAN for Joint Learning of Inpainting and its Spatial Optimisation [3.4392739159262145]
  We propose the first generative adversarial network for spatial inpainting data optimisation. In contrast to previous approaches, it allows joint training of an inpainting generator and a corresponding mask network. This yields significant improvements in visual quality and speed over conventional models and also outperforms current optimisation networks.
  arXiv  Detail & Related papers  (2022-02-11T14:02:36Z)
• Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid [102.24539566851809]
  Restoring reasonable and realistic content for arbitrary missing regions in images is an important yet challenging task. Recent image inpainting models have made significant progress in generating vivid visual details, but they can still lead to texture blurring or structural distortions. We propose the Semantic Pyramid Network (SPN) motivated by the idea that learning multi-scale semantic priors can greatly benefit the recovery of locally missing content in images.
  arXiv  Detail & Related papers  (2021-12-08T04:33:33Z)
• In&Out : Diverse Image Outpainting via GAN Inversion [89.84841983778672]
  Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image.
  arXiv  Detail & Related papers  (2021-04-01T17:59:10Z)
• Free-Form Image Inpainting via Contrastive Attention Network [64.05544199212831]
  In image inpainting tasks, masks with any shapes can appear anywhere in images which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. We propose a self-supervised Siamese inference network to improve the robustness and generalization.
  arXiv  Detail & Related papers  (2020-10-29T14:46:05Z)
• Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning [18.38266676724225]
  We propose a novel image outpainting method using bidirectional boundary region rearrangement. The proposed method is compared with other state-of-the-art outpainting and inpainting methods both qualitatively and quantitatively. The experimental results demonstrate that our method outperforms other methods and generates new images with 360degpanoramic characteristics.
  arXiv  Detail & Related papers  (2020-10-05T06:53:55Z)
• Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention [28.807711307545112]
  We propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Experiments on public datasets including CelebA-HQ, Places and Paris StreetView demonstrate that our model generates better inpainting results than the state-of-the-art competing algorithms.
  arXiv  Detail & Related papers  (2020-09-02T12:59:28Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.