HORIZON: High-Resolution Semantically Controlled Panorama Synthesis

• URL: http://arxiv.org/abs/2210.04522v2
• Date: Sat, 27 Jan 2024 08:42:29 GMT
• Title: HORIZON: High-Resolution Semantically Controlled Panorama Synthesis
• Authors: Kun Yan, Lei Ji, Chenfei Wu, Jian Liang, Ming Zhou, Nan Duan, Shuai Ma
• Abstract summary: Panorama synthesis endeavors to craft captivating 360-degree visual landscapes, immersing users in the heart of virtual worlds. Recent breakthroughs in visual synthesis have unlocked the potential for semantic control in 2D flat images, but a direct application of these methods to panorama synthesis yields distorted content. We unveil an innovative framework for generating high-resolution panoramas, adeptly addressing the issues of spherical distortion and edge discontinuity through sophisticated spherical modeling.
• Score: 105.55531244750019
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Panorama synthesis endeavors to craft captivating 360-degree visual landscapes, immersing users in the heart of virtual worlds. Nevertheless, contemporary panoramic synthesis techniques grapple with the challenge of semantically guiding the content generation process. Although recent breakthroughs in visual synthesis have unlocked the potential for semantic control in 2D flat images, a direct application of these methods to panorama synthesis yields distorted content. In this study, we unveil an innovative framework for generating high-resolution panoramas, adeptly addressing the issues of spherical distortion and edge discontinuity through sophisticated spherical modeling. Our pioneering approach empowers users with semantic control, harnessing both image and text inputs, while concurrently streamlining the generation of high-resolution panoramas using parallel decoding. We rigorously evaluate our methodology on a diverse array of indoor and outdoor datasets, establishing its superiority over recent related work, in terms of both quantitative and qualitative performance metrics. Our research elevates the controllability, efficiency, and fidelity of panorama synthesis to new levels.

Related papers

• ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis [63.169364481672915]
  We propose textbfViewCrafter, a novel method for synthesizing high-fidelity novel views of generic scenes from single or sparse images. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames.
  arXiv  Detail & Related papers  (2024-09-03T16:53:19Z)
• Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis [60.260724486834164]
  This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion framework. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.
  arXiv  Detail & Related papers  (2023-11-30T10:36:19Z)
• DeLiRa: Self-Supervised Depth, Light, and Radiance Fields [32.350984950639656]
  Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. Standard volume rendering approaches struggle with degenerate geometries in the case of limited viewpoint diversity. In this work, we propose to use the multi-view photometric objective as a geometric regularizer for volumetric rendering.
  arXiv  Detail & Related papers  (2023-04-06T00:16:25Z)
• Consistent View Synthesis with Pose-Guided Diffusion Models [51.37925069307313]
  Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications. We propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image.
  arXiv  Detail & Related papers  (2023-03-30T17:59:22Z)
• GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images [79.39247661907397]
  We introduce an effective framework Generalizable Model-based Neural Radiance Fields to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy.
  arXiv  Detail & Related papers  (2023-03-24T03:32:02Z)
• Semantic View Synthesis [56.47999473206778]
  We tackle a new problem of semantic view synthesis -- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input. First, we focus on synthesizing the color and depth of the visible surface of the 3D scene. We then use the synthesized color and depth to impose explicit constraints on the multiple-plane image (MPI) representation prediction process.
  arXiv  Detail & Related papers  (2020-08-24T17:59:46Z)
• GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis [43.4859484191223]
  We propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone.
  arXiv  Detail & Related papers  (2020-07-05T20:37:39Z)

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.