mlsanityData to 14 Oct 2026

FurryGAN: High Quality Foreground-aware Image Synthesis

arXiv 2208.10422 ↗First posted 22 Aug 2022cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 36 of its 42 references that are in the map. First posted on arXiv 22 Aug 2022.

Builds on

36 of 42 references.

References · 36

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24 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 1; 2026: 1.

6 papers.