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STATERA model estimates hidden mass from video using frozen temporal representations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed STATERA, a novel method for estimating the center-of-mass (CoM) of opaque, asymmetric rigid bodies from short monocular videos. This approach adapts a pretrained video backbone, V-JEPA, using mostly frozen weights and a lightweight temporal tubelet mixer to predict per-frame CoM heatmaps and trajectories. To facilitate this research, the HiddenMass Benchmark was created, featuring 50K simulated trajectories and a 63-sequence real-world test set with calibrated CoM ground truth. STATERA demonstrates improved accuracy in simulations and shows promise for zero-shot sim-to-real transfer, even when facing challenges with supervision signals. AI

IMPACT This research could advance the capabilities of AI in understanding physical properties from visual input, potentially impacting robotics and simulation.

RANK_REASON The cluster contains a research paper detailing a new model and benchmark for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

STATERA model estimates hidden mass from video using frozen temporal representations

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The cluster contains a research paper detailing a new model and benchmark for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Animesh Varma ·

    STATERA: Hidden Mass Estimation via Zero-Shot Sim-to-Real Kinematics using Frozen Temporal Tubelets

    arXiv:2610.00003v1 Announce Type: cross Abstract: Vision models pretrained for frame-level appearance often struggle to infer hidden physical properties from motion. We study center-of-mass (CoM) localization for opaque, asymmetric rigid bodies from short monocular videos, where …

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