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Abstract. Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

01.07.2020 · Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to …

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image ...

Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang. 16 Oct 2020NeurIPS 2020Readers: Everyone ...

Different from the recent mehtods, which directly synthesize the image from conditional input or look for meaningful editing operations in the existing latent ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two codes. The ...

01.07.2020 · Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an ...

01.07.2020 · manipulation of existing images remains challenging. We propose the Swapping. Autoencoder, a deep model designed specifically for image manipulation, rather. than random sampling. The key idea is ...

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The Swapping Autoencoder is proposed, a deep model designed specifically for image manipulation, rather than random sampling, ...

Abstract; Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.