Image animation brings life to the static object in the source image according to the driving video. Recent works attempt to perform motion transfer on arbitrary objects through unsupervised methods without using a priori knowledge. However, it remains a significant challenge for current unsupervised methods when there is a large pose gap between the objects in the source and driving images. In this paper, a new end-to-end unsupervised motion transfer framework is proposed to overcome such issue. Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image. Secondly, in order to restore the missing regions more realistically, we leverage multi-resolution occlusion masks to achieve more effective feature fusion. Finally, additional auxiliary loss functions are designed to ensure that there is a clear division of labor in the network modules, encouraging the network to generate high-quality images. Our method can animate a variety of objects, including talking faces, human bodies, and pixel animations. Experiments demonstrate that our method performs better on most benchmarks than the state of the art with visible improvements in pose-related metrics.
TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation
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）；Mashang Consumer Finance Co., Ltd（马上消费金融股份有限公司）
近年来，场景文本检测和识别的研究重点转向了任意形状的文本，其中文本形状表示是一个基本问题。一个理想的表示应该是紧凑的，完整的，有效的，并可重用的后续识别在我们看来。然而，以前的表述在一个或多个方面存在缺陷。薄板样条（TPS）变换在场景文本识别中取得了很大的成功。受此启发，我们重新思考了TPS的用法，并最终将TPS作为任意形状文本表示的一种精致表示。TPS表示是紧凑、完整和高效的。利用预测的TPS参数，可以直接将检测到的文本区域校正为近水平的文本区域，以辅助后续的识别。为了进一步利用TPS表示的潜力，提出了边界对齐损失。在此基础上，实现了文本检测器TPSNet，该文本检测器可以方便地扩展为文本检测器。广泛的评估和消融的几个公共基准证明了所提出的方法的有效性和优越性的文本表示和定位。特别是，TPSNet在Art数据集上实现了4.4\%的检测F-Measure改进（78.4\% vs. 74.0\%），在Total-Text上实现了5.0\%的端到端点样F-Measure改进（78.5\% vs. 73.5\%），这是大边距，没有花里胡哨的。
Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（先进信息科学与网络技术北京市重点实验室）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）
PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Department of Computer Science, Toronto Metropolitan University（计算机科学系，多伦多 Metropolitan 大学）；Department of Electrical Engineering, Faculty of Engineering, Assiut University（电气工程系，工程学院，阿西乌特大学）
Computer Vision Center（计算机视觉中心）；Univ. Autònoma de Barcelona (UAB)（巴塞罗那自治大学）；Universitat de Barcelona（巴塞罗那大学）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；University of Tartu（塔尔图大学）；Hasan kalyoncu University（哈桑·卡利翁库大学）
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
NORCE Norwegian Research Center AS（NORCE挪威研究中心）；National University of Science and Technology "Politehnica" Bucharest（布加勒斯特理工科学技术大学）；"Simion Stoilow" Institute of Mathematics of the Romanian Academy（罗马尼亚科学院西米翁斯托伊洛夫数学研究所）
EDFFDNet: Towards Accurate and Efficient Unsupervised Multi-Grid Image Registration
Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；NingboTech University（宁波科技大学）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology, Hangzhou, China（浙江大数据与未来电子商务技术重点实验室）
