ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
Dept. of Computer Science, University of Maryland, College Park, MD（计算机科学系，马里兰大学，学院公园，MD）；Dept. of Computational and Applied Mathematics, Rice University, Houston, TX（计算与应用数学系，里士满大学，休斯顿，TX）；Dept. of Mathematics, Massachusetts Institute of Technology, Cambridge, MA（数学系，麻省理工学院，剑桥，MA）；Dept. of Computer Science, University of California, Los Angeles, CA（计算机科学系，加州大学洛杉矶分校，CA）
Thomas Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto
We introduce a new method for location recovery from pair-wise directions that leverages an efficient convex program that comes with exact recovery guarantees, even in the presence of adversarial outliers. When pairwise directions represent scaled relative positions between pairs of views (estimated for instance with epipolar geometry) our method can be used for location recovery, that is the determination of relative pose up to a single unknown scale. For this task, our method yields performance comparable to the state-of-the-art with an order of magnitude speed-up. Our proposed numerical framework is flexible in that it accommodates other approaches to location recovery and can be used to speed up other methods. These properties are demonstrated by extensively testing against state-of-the-art methods for location recovery on 13 large, irregular collections of images of real scenes in addition to simulated data with ground truth.
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
Tsinghua University（清华大学）；The University of Sydney（悉尼大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Alibaba Group（阿里巴巴集团）；HKUST（香港科技大学）；Shandong University（山东大学）
MoSa: Motion Generation with Scalable Autoregressive Modeling
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School（人工智能通用基础理论国家重点实验室，北京大学深圳研究生院）；Chongqing University of Technology（重庆理工大学）；Tencent Technology Co., Ltd.（腾讯科技有限公司）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）
CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning
Nanjing University（南京大学）；Shanghai AI Lab（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；Tongji University（同济大学）
TRiGS: Temporal Rigid-Body Motion for Scalable 4D Gaussian Splatting
Sogang University（西江大学）；Pusan National University（釜山国立大学）；POSTECH（浦项科技大学）；Seoul National University（首尔国立大学）；Electronics and Telecommunications Research Institute（电子通信研究院）
