Rewriting Geometric Rules of a GAN

Sheng-Yu Wang	David Bau	Jun-Yan Zhu

ACM Transactions on Graphics (TOG) (2022)

Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss a key element of the creative process -- the ability to synthesize things that go far beyond the data distribution and everyday experience. To begin to address this issue, we enable a user to

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Sheng-Yu Wang, David Bau, Jun-Yan Zhu (2022). Rewriting Geometric Rules of a GAN. ACM Transactions on Graphics (TOG).

@article{wang2022rewriting,
author = {Sheng-Yu Wang and David Bau and Jun-Yan Zhu},
title = {Rewriting Geometric Rules of a GAN},
year = {2022},
journal = {ACM Transactions on Graphics (TOG)}
}