Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does not necessarily achieve locally sensible matchings. Concretely, two temporal points with entirely dissimilar local structures may be matched by DTW. To address this problem, we propose an improved alignment algorithm, named shape Dynamic Time Warping (shapeDTW), which enhances DTW by taking point-wise local structural information into consideration. shapeDTW is inherently a DTW algorithm, but additionally attempts to pair locally similar structures and to avoid matching points with distinct neighborhood structures. We apply shapeDTW to align audio signal pairs having ground-truth alignments, as well as artificially simulated pairs of aligned sequences, and obtain quantitatively much lower alignment errors than DTW and its two variants. When shapeDTW is used as a distance measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series, it beats DTW on 64 out of 84 UCR time series datasets, with significantly improved classification accuracies. By using a properly designed local structure descriptor, shapeDTW improves accuracies by more than 10% on 18 datasets. To the best of our knowledge, shapeDTW is the first distance measure under the nearest neighbor classifier scheme to significantly outperform DTW, which had been widely recognized as the best distance measure to date. Our code is publicly accessible at: https://github.com/jiapingz/shapeDTW.
Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
我们在一个统一的框架内处理人体图像合成，包括人体运动模仿，外观转移和新颖的视图合成。这意味着模型一旦经过训练，就可以用来处理所有这些任务。现有的特定任务的方法主要使用2D关键点来估计人体结构。然而，他们只表示的位置信息，没有能力来表征个性化的形状的人和模型的肢体旋转。在本文中，我们建议使用一个三维人体网格恢复模块来解开姿势和形状。它不仅可以模拟关节的位置和旋转，而且可以表征个性化的身体形状。为了保留源信息，如纹理，风格，颜色和人脸身份，我们提出了一个注意液体Warping GAN与注意液体Warping块（AttLWB），传播图像和特征空间的源信息的合成参考。具体地说，源特征提取的去噪卷积自动编码器的特征源身份很好。此外，我们提出的方法可以支持更灵活的多个来源的扭曲。为了进一步提高不可见源图像的泛化能力，应用了一次/Few-Shot对抗学习。具体来说，它首先在一个广泛的训练集中训练一个模型。然后，它以自我监督的方式通过一个/Few-Shot不可见图像对模型进行微调，以生成高分辨率（512 x 512和1024 x 1024）结果。此外，我们建立了一个新的数据集，即iPER数据集，用于评估人体运动模仿，外观转移和新的视图合成。大量的实验证明了我们的方法在保持人脸身份，形状一致性和衣服细节方面的有效性。所有代码和数据集都可以在https://impersonator.org/work/…
Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping
Facultad de Ciencias de la Electrónica Benemérita Universidad Autónoma de Puebla（Benemérita自治大学电子科学学院）
FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
EPFL（苏黎世联邦理工学院）；University of Buenos Aires（布宜诺斯艾利斯大学）；University of São Paulo（圣保罗大学）；IMPA（巴西科学院）；Universidade Federal Rural de Pernambuco（皮钦查联邦农业大学）；ISR-UC（UC ISR）；INCM；University of Georgia（佐治亚大学）
A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures
Murdoch University（默多克大学）；University of Western Australia（西澳大学）；Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China（电子科技大学长三角研究院（湖州））；Florida State University（佛罗里达州立大学）
TRACE: Artifact-Robust Statistical Shape Modeling from Imperfect Surface Scans - A Case Study in Craniosynostosis 3D Photography
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Division of Pediatric Plastic Surgery, UPMC Children’s Hospital of Pittsburgh（匹兹堡大学医学中心儿童医院小儿整形外科）
