360 images represent scenes captured in all possible viewing directions. They enable viewers to navigate freely around the scene and thus provide an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only some parts of the scenes are observed, and valuable information about the surroundings is lost. In this paper, we propose a learning-based approach that reconstructs the scene in 360 x 180 from a sparse set of conventional images (typically 4 images). The proposed approach first estimates the FOV of input images relative to the panorama. The estimated FOV is then used as the prior for synthesizing a high-resolution 360 panoramic output. The proposed method overcomes the difficulty of learning-based approach in synthesizing high resolution images (up to 512 x 1024). Experimental results demonstrate that the proposed method produces 360 panorama with reasonable quality. It is shown that it outperforms the alternative method and can be generalize for non-panorama scene and images captured by a smartphone camera.
Image-to-image translation proposed by Isola al @cite_7 translates input domain to another domain using input and output for training. This work was extended by Wang al @cite_15 to synthesize high-resolution images from input semantic labels. Despite the results, their work relied on semantic labels which are not always available and practical for most inputs. Karras al @cite_11 obtained impressive results in generating images with high-resolution using GAN, trained in an unconditional manner. The network can synthesize facial images with great details but it requires a large amount of computing power.
@cite_15 @cite_7 @cite_11
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We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 A— 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either. We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.