# cFineGAN: Unsupervised multi-conditional fine-grained image generation

Source: [cFineGAN: Unsupervised multi-conditional fine-grained image generation](<https://www.sota2.com/research/paper/68fa66c81e209131cea123a8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:58:21.378Z

Authors: Gunjan Aggarwal, Abhishek Sinha

Published: 2019-12-06T04:16:08.000Z

## Abstract

We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves the texture of one and the shape of the other input. To achieve this goal, we extend upon the recently proposed work of FineGAN \\citep{singh2018finegan} and make use of standard as well as shape-biased pre-trained ImageNet models. We demonstrate both qualitatively as well as quantitatively the benefit of using the shape-biased network. We present our image generation result across three benchmark datasets- CUB-200-2011, Stanford Dogs and UT Zappos50k.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.05028>)

[PDF](<https://arxiv.org/pdf/1912.05028.pdf>)