Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping. Few-shot unsupervised image-to-image translation further attempts to generalize the model to an unseen domain by leveraging example images of the unseen domain provided at inference time. While remarkably successful, existing few-shot image-to-image translation models find it difficult to preserve the structure of the input image while emulating the appearance of the unseen domain, which we refer to as the content loss problem. This is particularly severe when the poses of the objects in the input and example images are very different. To address the issue, we propose a new few-shot image translation model, COCO-FUNIT, which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias. Through extensive experimental validations with comparison to the state-of-the-art, our model shows effectiveness in addressing the content loss problem. For code and pretrained models, please check out https://nvlabs.github.io/COCO-FUNIT/ .
Facebook AI Research（Facebook人工智能研究院）；École Polytechnique de Montréal（蒙特利尔理工学院）；Mila, Quebec AI Institute（米拉-魁北克人工智能研究所）；Télécom Paris（巴黎电信学院）；McGill University（麦吉尔大学）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
University of Science and Technology of China（中国科学技术大学）；College of Software, Beihang University（北京航空航天大学软件学院）；SenseTime Research（商汤科技研究院）；The Chinese Academy of Sciences（中国科学院）；The Chinese University of Hong Kong（香港中文大学）
University of Surrey（萨里大学）；iFlyTek-Surrey Joint Research Centre on Artificial Intelligence（讯飞-萨里人工智能联合研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Twente（特温特大学）
