TSIT: A Simple and Versatile Framework for Image-to-Image Translation

• URL: http://arxiv.org/abs/2007.12072v2
• Date: Sat, 25 Jul 2020 11:20:38 GMT
• Title: TSIT: A Simple and Versatile Framework for Image-to-Image Translation
• Authors: Liming Jiang, Changxu Zhang, Mingyang Huang, Chunxiao Liu, Jianping Shi, Chen Change Loy
• Abstract summary: We introduce a simple and versatile framework for image-to-image translation. We provide a carefully designed two-stream generative model with newly proposed feature transformations. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.
• Score: 103.92203013154403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a simple and versatile framework for image-to-image translation. We unearth the importance of normalization layers, and provide a carefully designed two-stream generative model with newly proposed feature transformations in a coarse-to-fine fashion. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network, permitting our method to scale to various tasks in both unsupervised and supervised settings. No additional constraints (e.g., cycle consistency) are needed, contributing to a very clean and simple method. Multi-modal image synthesis with arbitrary style control is made possible. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.

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