StyleLight: HDR Panorama Generation for Lighting Estimation and Editing

• URL: http://arxiv.org/abs/2207.14811v1
• Date: Fri, 29 Jul 2022 17:58:58 GMT
• Title: StyleLight: HDR Panorama Generation for Lighting Estimation and Editing
• Authors: Guangcong Wang and Yinuo Yang and Chen Change Loy and Ziwei Liu
• Abstract summary: We present a new lighting estimation and editing framework to generate high-dynamic-range (GAN) indoor panorama lighting from a single field-of-view (LFOV) image. Our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation.
• Score: 98.20167223076756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 focal-masked GAN inversion method to find its latent code by the LDR panorama synthesis branch and then synthesize the HDR panorama by the HDR panorama synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting generation into a unified framework and thus greatly improves lighting estimation. Extensive experiments demonstrate that our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation. Notably, StyleLight also enables intuitive lighting editing on indoor HDR panoramas, which is suitable for real-world applications. Code is available at https://style-light.github.io.

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