TL;DR
This paper introduces PanoDiff, a novel pipeline that generates high-quality 360-degree panoramas from few unregistered NFoV images using a two-stage angle prediction and a diffusion-based model with control signals.
Contribution
The paper presents a new method combining angle prediction and diffusion models to generate controllable panoramas from arbitrary NFoV images, improving quality and flexibility.
Findings
Achieves state-of-the-art panorama generation quality
Provides high controllability with control signals
Handles various numbers of NFoV inputs effectively
Abstract
360 panoramas are extensively utilized as environmental light sources in computer graphics. However, capturing a 360 180 panorama poses challenges due to the necessity of specialized and costly equipment, and additional human resources. Prior studies develop various learning-based generative methods to synthesize panoramas from a single Narrow Field-of-View (NFoV) image, but they are limited in alterable input patterns, generation quality, and controllability. To address these issues, we propose a novel pipeline called PanoDiff, which efficiently generates complete 360 panoramas using one or more unregistered NFoV images captured from arbitrary angles. Our approach has two primary components to overcome the limitations. Firstly, a two-stage angle prediction module to handle various numbers of NFoV inputs. Secondly, a novel latent diffusion-based…
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