arXiv AI
SituHOI: Scalable Capture of Scene-Situated Full-Body Human-Object Interaction
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DewHand: Direct Metric World-Space Hand Motion Reconstruction from Egocentric Videos
arXiv:2610.29936v1 Announce Type: new Abstract: Recovering metric world-space hand motion from egocentric video supports understanding human interactions and transferring them to embodied agents. Thr...
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Oct 8
ECHO: Embodied Camera Observations of Human Object Carrying
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ECHO: Embodied Camera Observations of Human Object Carrying
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people who live in it. Progre...
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TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
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.
arXiv AI
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WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model
WildHSR introduces a lightweight adaptation of 3D foundation models to jointly recover metric cameras, scene geometry, and persistent person identities from monocular video. By generating pseudo‑scale labels from curated web footage and fine‑tuning a Scale Readout, the method predicts metric scale directly from foundation‑model tokens. It also exploits intermediate query‑key features to associate per‑frame bodies, enabling feed‑forward reconstruction that outperforms state‑of‑the‑art optimization‑based methods on several benchmarks while running at 10.1 fps.
arXiv Computer Vision
Sep 17
Track, Articulate, Act: Generating Articulation from Casual Human Videos
arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes...