The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learning a reconstruct and align approach. Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose. In particular, we propose learning two networks: the first maps images to a 3D geometry-aware feature bottleneck and is trained via an image-to-image translation loss; the second learns whether two instances of features are aligned. At test time, our model finds the relative transformation that best aligns the bottleneck features of our test image to a reference image. We evaluate our method on novel object viewpoint estimation by generalizing across different datasets, analyzing the impact of our different modules, and providing a qualitative analysis of the learned features to identify what representations are being learnt for alignment.
MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
Coarse-Fine View Attention Alignment-Based GAN for CT Reconstruction from Biplanar X-Rays
Beijing United Imaging Research Institute of Intelligent Imaging（北京联影智能影像研究院）；United Imaging Intelligence Beijing（联影智能（北京））；Peking University Third Hospital（北京大学第三医院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
Swiss Data Science Center (SDSC) in Paul Scherrer Institute (PSI)（瑞士数据科学中心（SDSC）在保罗·舍勒研究所（PSI））；Swiss Data Science Center (SDSC) and ETH Zurich（瑞士数据科学中心（SDSC）和苏黎世联邦理工学院）；Swiss Data Science Center (SDSC) and Paul Scherrer Institute (PSI)（瑞士数据科学中心（SDSC）和保罗·舍勒研究所（PSI））
SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造学域）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；The Hong Kong University of Science and Technology（香港科技大学）
SoLAR: Error-Resilient Streamable Long-Horizon Free-Viewpoint Video Reconstruction with Anchor Activation and Latent Recalibration
National Engineering Laboratory for Video Technology, School of Computer Science, Peking University（国家视频技术工程实验室，计算机科学学院，北京大学）；School of Computer Science, Jilin University（吉林大学计算机科学学院）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute, Northwestern Polytechnical University（西北工业大学宁波研究院）；Nanyang Technological University（南洋理工大学）；The University of Adelaide（阿德莱德大学）
Motion Matters: Compact Gaussian Streaming for Free-Viewpoint Video Reconstruction
College of Applied Technology, Shenzhen University（深圳大学应用技术学院）；College of Big Data and Internet, Shenzhen Technology University（深圳科技大学大数据与互联网学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Pengcheng Laboratory, Shenzhen（深圳鹏城实验室）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；School of Electronic and Information Engineering, Foshan University（电子与信息工程学院，佛山大学）
PAS3R: Pose-Adaptive Streaming 3D Reconstruction for Long Video Sequences
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China（人机混合增强智能国家重点实验室，人工智能与机器人研究院，西安交通大学，中国）；University of East Anglia, Norwich, NR47TJ, United Kingdom（东安格利亚大学，诺里奇，英国）
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition
Australian National University（澳大利亚国立大学）；Data61/CSIRO（澳大利亚联邦科学与工业研究组织数据61研究所）
我们提出了一个少样本学习管道的联合tEmporal和cAmera的视点算法（JEANIE）的三维基于动作的动作识别。为了排除查询和支持序列之间的3D身体关节的不对齐，我们提出了一个先进的动态时间规整的变种，它联合建模查询和支持帧之间的每个平滑路径，同时实现最佳的对齐时间和模拟相机的视点空间有限的Few-Shot训练数据下的端到端学习。基于简单谱图卷积（一种轻量级线性图神经网络骨干）的时间块编码器对序列进行编码。我们还包括一个带有Transformer的设置。最后，我们提出了一个基于相似性的损失，鼓励对齐的序列相同的类，同时防止不相关的序列的对齐。我们展示了NTU-60，NTU-120，Kinetics骨架和UWA 3D Multiview Activity II的最新结果。
MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
Seoul National University, Seoul, South Korea（首尔国立大学，首尔，韩国）；Konkuk University, Seoul, South Korea（韩国konkuk大学，首尔，韩国）；Microsoft Research Asia, Beijing, China（微软亚洲研究院，北京，中国）；HodooAI Labs, Seoul, South Korea（HodooAI实验室，首尔，韩国）
A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction
Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig（布伦瑞克工业大学测量与摄影测量研究所）；University of Tehran（德黑兰大学）；Intelligent Robotics Research Division, ETRI（韩国电子通信研究院智能机器人研究部）；New York University（纽约大学）；University of Edinburgh（爱丁堡大学）
Towards Arbitrary-View Face Alignment by Recommendation Trees
The Chinese University of Hong Kong（香港中文大学）；SenseTime Group（商汤集团）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
