Johns Hopkins University（约翰斯·霍普金斯大学）

TGT: Text-Grounded Trajectories for Locally Controlled Video Generation

Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang, Liming Jiang, Haotian Yang, Bo Liu, Yiding Yang, Guang Chen, Longyin Wen, Alan Yuille, Chongyang Ma

机构 * Johns Hopkins University（约翰霍普金斯大学） ； Bytedance, Intelligent Creation（字节跳动，智能创作） 纠错

Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields

Yi Cao, Paulette Clancy

机构 * Johns Hopkins University（约翰霍普金斯大学） 纠错

Comments 10 pages (+Appendix 22 pages), 8 figures. To appear in 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: AI for Accelerated Materials Discovery (AI4Mat) Workshop. Code and datasets available at https://github.com/yicao-elina/MigrationBench.git

Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs

My Le, Luana Ruiz, Souvik Dhara

机构 * Dept. of Applied Mathematics & Statistics（应用数学与统计学系） ； Johns Hopkins University（约翰霍普金斯大学） ； School of Industrial and Systems Engineering（工业与系统工程学院） ； Georgia Institute of Technology（佐治亚理工学院） 纠错

Comments Landmark-based embeddings preserve global distances in graphs more efficiently; on Erdos-Renyi random graphs they need lower dimensions, and GNNs generalize these embeddings to large real-world networks

X$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction

Weihao Yu, Yuanhao Cai, Ruyi Zha, Zhiwen Fan, Chenxin Li, Yixuan Yuan

机构 * The Chinese University of Hong Kong（香港中文大学） ； Johns Hopkins University（约翰霍普金斯大学） ； The Australian National University（澳大利亚国立大学） ； University of Texas at Austin（德克萨斯大学奥斯汀分校） 纠错

Comments Project Page: https://x2-gaussian.github.io/

Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models

Xingrui Wang, Wufei Ma, Tiezheng Zhang, Celso M de Melo, Jieneng Chen, Alan Yuille

机构 * Johns Hopkins University（约翰霍普金斯大学） ； DEVCOM Army Research Laboratory（陆军研究实验室） 纠错

Comments Published in CVPR 2025 as Highlight. Data and code are released at https://github.com/XingruiWang/Spatial457