Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer

Author:

Kim Sunwoo,Kim Soohyun,Kim Seungryong

Abstract

Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Introducing Intermediate Domains for Effective Self-Training during Test-Time;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Shuttling Through Films: A Recoloring Method Based on Chinese Film Aesthetics;2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC);2023-12-08

3. Line Search-Based Feature Transformation for Fast, Stable, and Tunable Content-Style Control in Photorealistic Style Transfer;2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2023-01

4. Unsupervised Scene Sketch to Photo Synthesis;Lecture Notes in Computer Science;2023

5. ArtFID: Quantitative Evaluation of Neural Style Transfer;Lecture Notes in Computer Science;2022

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