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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 3: AAAI-21 Technical Tracks 3

Self-Supervised Sketch-to-Image Synthesis

February 1, 2023

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Authors

Bingchen Liu

Playform - Artrendex Inc., USA Department of Computer Science, Rutgers University


Yizhe Zhu

Department of Computer Science, Rutgers University


Kunpeng Song

Playform - Artrendex Inc., USA Department of Computer Science, Rutgers University


Ahmed Elgammal

Playform - Artrendex Inc., USA Department of Computer Science, Rutgers University


DOI:

10.1609/aaai.v35i3.16304


Abstract:

Imagining a colored realistic image from an arbitrary-drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycle-consistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE's superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on $1024^2$ resolution demonstrate a new state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which the synthesized images are not only style-consistent but also semantically meaningful.

Topics: AAAI

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HOW TO CITE:

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal Self-Supervised Sketch-to-Image Synthesis Proceedings of the AAAI Conference on Artificial Intelligence (2021) 2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal Self-Supervised Sketch-to-Image Synthesis AAAI 2021, 2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal (2021). Self-Supervised Sketch-to-Image Synthesis. Proceedings of the AAAI Conference on Artificial Intelligence, 2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. Self-Supervised Sketch-to-Image Synthesis. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. 2021. Self-Supervised Sketch-to-Image Synthesis. "Proceedings of the AAAI Conference on Artificial Intelligence". 2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. (2021) "Self-Supervised Sketch-to-Image Synthesis", Proceedings of the AAAI Conference on Artificial Intelligence, p.2073-2081

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal, "Self-Supervised Sketch-to-Image Synthesis", AAAI, p.2073-2081, 2021.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. "Self-Supervised Sketch-to-Image Synthesis". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. "Self-Supervised Sketch-to-Image Synthesis". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 2073-2081.

Bingchen Liu||Yizhe Zhu||Kunpeng Song||Ahmed Elgammal. Self-Supervised Sketch-to-Image Synthesis. AAAI[Internet]. 2021[cited 2023]; 2073-2081.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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