Aligning Latent and Image Spaces to Connect the Unconnectable

• URL: http://arxiv.org/abs/2104.06954v1
• Date: Wed, 14 Apr 2021 16:29:20 GMT
• Title: Aligning Latent and Image Spaces to Connect the Unconnectable
• Abstract summary: We develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous equis in the image and latent spaces. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times.
• Score: 22.90522514729253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the nearby style codes. We modify the AdaIN mechanism to work in such a setup and train the generator in an adversarial setting to produce images positioned between any two latent vectors. At test time, this allows for generating complex and diverse infinite images and connecting any two unrelated scenes into a single arbitrarily large panorama. Apart from that, we introduce LHQ: a new dataset of \lhqsize high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project page is located at https://universome.github.io/alis.

Related papers

• Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis [19.89638839762492]
  Genesis is a generative engine that brings both axes together by composing two specialized operators over the quadtree.<n>It propagates sparse seeds into seamless, multi-resolution maps from any initial configuration.<n> dense500 is a fully observed multi-scale pyramid dataset spanning diverse geographic regions.
  arXiv  Detail & Related papers  (2026-09-02T14:53:21Z)
• InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis [49.87457834078904]
  InfiniSplat is a feed-forward single-image 3DGS framework that moves from a pixel-aligned representation toward a surface-aligned representation.<n>InfiniSplat achieves state-of-the-art performance compared with single-image feed-forward baselines.
  arXiv  Detail & Related papers  (2026-08-03T16:17:39Z)
• LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving [7.999454304974351]
  LoomNet is a novel multi-view diffusion architecture that produces coherent images by applying the same diffusion model multiple times.<n>In experiments, LoomNet outperforms state-of-the-art methods on both image quality and reconstruction metrics.
  arXiv  Detail & Related papers  (2025-07-07T21:46:50Z)
• MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation [54.64194935409982]
  We introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer-wise RGBA decompositions. MuLAn is the first photorealistic resource providing instance decomposition and spatial information for high quality images. We aim to encourage the development of novel generation and editing technology, in particular layer-wise solutions.
  arXiv  Detail & Related papers  (2024-04-03T14:58:00Z)
• CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying [52.91778151771145]
  In this paper, we try to break the limitations for the first time thanks to the recent development of continuous implicit representation. Experiments show that the proposed method achieves real-time performance on the 2048$times$2048 images using a single GTX 2080 Ti GPU.
  arXiv  Detail & Related papers  (2023-03-15T11:13:51Z)
• High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets [39.21692649763314]
  In this paper, we present a novel GAN inversion framework that utilizes the powerful generative ability of StyleGAN-XL. To ease the inversion challenge with StyleGAN-XL, Clustering & Regularize Inversion (CRI) is proposed. We validate our CRI scheme on multiple restoration tasks (i.e., inpainting, colorization, and super-resolution) of complex natural images, and show preferable quantitative and qualitative results.
  arXiv  Detail & Related papers  (2023-02-07T11:24:11Z)
• Conditional Progressive Generative Adversarial Network for satellite image generation [0.7734726150561089]
  We formulate the image generation task as completion of an image where one out of three corners is missing. We then extend this approach to iteratively build larger images with the same level of detail. Our goal is to obtain a scalable methodology to generate high resolution samples typically found in satellite imagery data sets.
  arXiv  Detail & Related papers  (2022-11-28T13:33:53Z)
• Any-resolution Training for High-resolution Image Synthesis [55.19874755679901]
  Generative models operate at fixed resolution, even though natural images come in a variety of sizes. We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions. We introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions.
  arXiv  Detail & Related papers  (2022-04-14T17:59:31Z)
• Local and Global GANs with Semantic-Aware Upsampling for Image Generation [201.39323496042527]
  We consider generating images using local context. We propose a class-specific generative network using semantic maps as guidance. Lastly, we propose a novel semantic-aware upsampling method.
  arXiv  Detail & Related papers  (2022-02-28T19:24:25Z)
• Collaging Class-specific GANs for Semantic Image Synthesis [68.87294033259417]
  We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. Experiments show that our approach can generate high quality images in high resolution.
  arXiv  Detail & Related papers  (2021-10-08T17:46:56Z)
• InfinityGAN: Towards Infinite-Resolution Image Synthesis [92.40782797030977]
  We present InfinityGAN, a method to generate arbitrary-resolution images. We show how it trains and infers patch-by-patch seamlessly with low computational resources.
  arXiv  Detail & Related papers  (2021-04-08T17:59:30Z)
• Using latent space regression to analyze and leverage compositionality in GANs [33.381584322411626]
  We investigate regression into the latent space as a probe to understand the compositional properties of GANs. We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space.
  arXiv  Detail & Related papers  (2021-03-18T17:58:01Z)
• Adversarial Generation of Continuous Images [31.92891885615843]
  In this paper, we propose two novel architectural techniques for building INR-based image decoders. We use them to build a state-of-the-art continuous image GAN. Our proposed INR-GAN architecture improves the performance of continuous image generators by several times.
  arXiv  Detail & Related papers  (2020-11-24T11:06:40Z)

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.