BlobGAN: Spatially Disentangled Scene Representations

@article{Epstein2022BlobGANSD,
  title={BlobGAN: Spatially Disentangled Scene Representations},
  author={Dave Epstein and Taesung Park and Richard Zhang and Eli Shechtman and Alexei A. Efros},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.02837}
}
. We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes are modeled as a collection of spatial, depth-ordered “blobs” of features. Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network. Due to the spatial uniformity of blobs and the locality inherent to convolution, our network learns to associate different… 
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Enriching StyleGAN with Illumination Physics
TLDR
This paper shows how to use simple physical properties of images to enrich StyleGAN’s generation capacity, and suggests the proposed method, StyLitGAN, can add and remove luminaires in the scene and generate images with realistic lighting effects, requiring no labeled paired relighting data or any other geometric supervision.
Rough bibliographical notes on intrinsic images, equivariance and relighting
  • Computer Science
  • 2022
TLDR
The WHDR evaluation framework was put in place by [12], who constructed a dataset consisting of human judgements which compare the absolute lightness at pairs of points in real images, and is known as the weighted human disagreement ratio (WHDR).

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