Bokeh effect is an optical phenomenon that offers a pleasant visual
experience, typically generated by high-end cameras with wide aperture lenses.
The task of bokeh effect transformation aims to produce a desired effect in one
set of lenses and apertures based on another combination. Current models are
limited in their ability to render a specific set of bokeh effects, primarily
transformations from sharp to blur. In this paper, we propose a novel universal
method for embedding lens metadata into the model and introducing a loss
calculation method using alpha masks from the newly released Bokeh Effect
Transformation Dataset(BETD) [3]. Based on the above techniques, we propose the
BokehOrNot model, which is capable of producing both blur-to-sharp and
sharp-to-blur bokeh effect with various combinations of lenses and aperture
sizes. Our proposed model outperforms current leading bokeh rendering and image
restoration models and renders visually natural bokeh effects. Our code is
available at: this https URL

本论文提出了一种新的通用方法，在模型中嵌入镜头元数据，并引入了一种来自新发布的 Bokeh Effect Transformation Dataset（BETD）[3] 的 alpha masks 的损失计算方法。基于以上技术，我们提出了 BokehOrNot 模型，能够产生各种组合的透镜和光圈大小的模糊到清晰、清晰到模糊的浅景深特效，其效果比当前领先的浅景深渲染和图像修复模型都要好，并呈现出自然的视觉效果。

使用图像变换器和镜头元数据嵌入转换背景虚化效果

BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens  Metadata Embedding

Foreground-aware image synthesis aims to generate images as well as their
foreground masks. A common approach is to formulate an image as an masked
blending of a foreground image and a background image. It is a challenging
problem because it is prone to reach the trivial solution where either image
overwhelms the other, i.e., the masks become completely full or empty, and the
foreground and background are not meaningfully separated. We present FurryGAN
with three key components: 1) imposing both the foreground image and the
composite image to be realistic, 2) designing a mask as a combination of coarse
and fine masks, and 3) guiding the generator by an auxiliary mask predictor in
the discriminator. Our method produces realistic images with remarkably
detailed alpha masks which cover hair, fur, and whiskers in a fully
unsupervised manner.

FurryGAN 是一种前景感知图像合成方法，通过引入具有粗细融合的蒙版、辅助蒙版预测器以及使得前景图像和合成图像都具有逼真性等关键组件，能够在无需人工监督的情况下生成具有毛发、皮毛和胡须的逼真图像和详细的透明度蒙版。