SituHOI is a large‑scale RGB‑D dataset that captures full‑body human‑object interactions in 16 indoor rooms, comprising 18,429 annotated clips and about 86 hours of activity. The dataset pairs moving‑camera recordings with static scene scans, object geometry, language descriptions, and estimated human and object trajectories, all registered in a shared floor‑aligned frame via a pipeline that integrates camera localization, depth‑based alignment, human mesh recovery, and object tracking. The data enable the FlexHOI‑Bench benchmark for goal‑conditioned joint motion prediction, and a reference predictor, SharedHOI, demonstrates strong performance on world‑space accuracy and human‑object coordination.
whyItMatters":"The dataset provides scalable, scene‑aligned supervision for modeling human‑object interaction, addressing a key limitation of existing datasets and enabling more accurate motion prediction benchmarks."
By Qifan Wang, Qihang Fang, Boao Shi, Yushi Du, Pei Zhou, Siyan Dong, Yanchao Yang
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
By Skanda Koppula, Frano Rajic, Abdullah Faiz Ur Rahman, Yi Yang, Ignacio Rocco, Jeet Thakwani, Rishabh Kabra, Andrew Zisserman, Joao Carreira, Siyu Tang, Carl Doersch, Gabriel Brostow
MetricTrail is a streaming system that accurately estimates camera motion and performs dense 3D reconstruction from long monocular videos. It leverages single‑image metric depth to improve trajectory accuracy without retraining the correspondence network or enlarging the optimization window, and uses learned adapters to enhance feature matching with frozen DINOv3 representations. The modular design allows independent upgrades of the depth provider, supports supplied camera intrinsics and calibrated stereo data, and achieves the lowest average trajectory error on KITTI, Oxford Spires, and VBR under RGB‑only protocols while delivering strong dense reconstructions on indoor, outdoor, and in‑the‑wild benchmarks.
By Jingnan Gao, Jiahui Huang, Sven Elflein, Jianhao Zheng, Bingxin Ke, Xiaokang Yang, Yichao Yan, Laura Leal-Taix\'e, Qunjie Zhou, Jiawei Ren, Shengyu Huang
arXiv:2603.12064v3 Announce Type: replace
Abstract: We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras -- a settin...
By Shuo Sun, Unal Artan, Malcolm Mielle, Achim J. Lilienthaland, Martin Magnusson
arXiv:2603.17068v3 Announce Type: replace
Abstract: Structured 3D representations such as keypoints and meshes offer compact, expressive descriptions of deformable objects, jointly capturing geometri...
By Yeheng Zong, Yizhou Chen, Alexander Bowler, Chia-Tung Yang, Ram Vasudevan
arXiv:2609.01276v1 Announce Type: new
Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
By Kai Guan, Minchao Jiang, Ruichen WangLi, Wentao Zhu, Lei Zhang