# BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh

arXiv: https://arxiv.org/abs/2011.02242 · HTML: https://mlsanity.com/paper/2011.02242/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [BokehMe: When Neural Rendering Meets Classical Rendering](https://mlsanity.com/paper/2206.12614/) (2022)
- [DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields](https://mlsanity.com/paper/2208.00945/) (2022)
- [MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects](https://mlsanity.com/paper/2207.08403/) (2022)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [Bokehlicious: Photorealistic Bokeh Rendering with Controllable Apertures](https://mlsanity.com/paper/2503.16067/) (2025)
- [Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors](https://mlsanity.com/paper/2306.04506/) (2023)
- [BokehDiff: Neural Lens Blur with One-Step Diffusion](https://mlsanity.com/paper/2507.18060/) (2025)
- [DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.00746/) (2025)
- [The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies](https://mlsanity.com/paper/2301.09545/) (2023)
- [Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks](https://mlsanity.com/paper/2106.13041/) (2021)
- [Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network](https://mlsanity.com/paper/2111.12971/) (2021)
- [Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh](https://mlsanity.com/paper/2208.12343/) (2022)
- [Adaptive Mask-based Pyramid Network for Realistic Bokeh Rendering](https://mlsanity.com/paper/2210.16078/) (2022)
- [BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata Embedding](https://mlsanity.com/paper/2306.04032/) (2023)
- [Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report](https://mlsanity.com/paper/2211.06769/) (2022)
- [Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance](https://mlsanity.com/paper/2410.14400/) (2024)
- [Boosting Monocular Metric Depth Estimation via Bokeh Rendering](https://mlsanity.com/paper/2512.12425/) (2025)
- [GBSD: Generative Bokeh with Stage Diffusion](https://mlsanity.com/paper/2306.08251/) (2023)
- [Dual-Camera All-in-Focus Neural Radiance Fields](https://mlsanity.com/paper/2504.16636/) (2025)

## Abstract (arXiv)

A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. However, due to the limitation of sensors, smartphones cannot capture images with depth-of-field effects directly. In this paper, we propose a novel generator called Glass-Net, which generates bokeh images not relying on complex hardware. Meanwhile, the GAN-based method and perceptual loss are combined for rendering a realistic bokeh effect in the stage of finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in our network, which ensures our tflite model with IN can be accelerated on smartphone GPU. Experiments show that our method is able to render a high-quality bokeh effect and process one 1024 × 1536 pixel image in 1.9 seconds on all smartphone chipsets. This approach ranked First in AIM 2020 Rendering Realistic Bokeh Challenge Track 1 & Track 2.