StyleLight: HDR Panorama Generation for Lighting Estimation and Editing
Guangcong Wang, Yinuo Yang, Chen Change Loy, Ziwei Liu

TL;DR
StyleLight is a novel framework that generates HDR indoor panoramas from limited LDR images, improving lighting estimation accuracy and enabling intuitive editing for real-world applications.
Contribution
It introduces a coupled dual-StyleGAN network that unifies LDR and HDR panorama synthesis, enhancing indoor lighting estimation from limited views.
Findings
Outperforms state-of-the-art lighting estimation methods.
Enables realistic HDR panorama generation from single LDR images.
Supports intuitive lighting editing for practical applications.
Abstract
We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decompose this problem into LFOV-to-panorama and LDR-to-HDR lighting generation sub-tasks. However, due to the partial observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a scene, lighting estimation remains a challenging task. To tackle this problem, we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that integrates LDR and HDR panorama synthesis into a unified framework. The LDR and HDR panorama synthesis share a similar generator but have separate discriminators. During inference, given an LDR LFOV image, we propose a…
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Taxonomy
TopicsAdvanced Vision and Imaging · Image Enhancement Techniques · Computer Graphics and Visualization Techniques
