Recently, image-to-video person reidentification (IVPR) has attracted enormous research interest, and various models are proposed. IVPR is often applied to urgent situations, such as suspect tracking and lost-human locating. Existing IVPR models are under supervised frameworks, which require a large number of labeled image-to-video pairs. This severely limits their real-time efficiency in urgent situations, because annotation is much more time-consuming. To solve the urgent image-to-video person reidentification (UIVPR) problem, we propose a cross-media transfer cycle generative adversarial networks (CTC-GAN) network. Our model aims to alleviate the “media-gap” between image-to-video pairs without newly labeled pairs. We make an existing completely labeled dataset as guidance for CTC-GAN to achieve domain adaptation and make urgent image-to-video matching easier for person reidentification. We introduce cycle GANs for image(video)-to-video(image) translation and extract cross-media features using a triplet constraint in the source domain for different media features. Furthermore, we train the model in the labeled source domain by reconstructing the image (video) as its related video (image). Then, train the model in the unlabeled target domain by reconstructing itself along with source data, so as to ensure that the discriminative model can be used in target domain. Through CTC-GAN, our network can retain pedestrian discriminative information as much as possible, to ensure the matching rate in the target domain. To validate the effectiveness of our approach, we implement substantial experiments on two large-scale person reidentification datasets compared with six existing state-of-the-art unsupervised revised person reidentification models, and experimental results demonstrate that our method can solve UIVPR effectively.