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Track, Articulate, Act: Generating Articulation from Casual Human Videos

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes in object state. In this work, we study articulated objects such as doors, drawers, cabinets, laptops, ovens, and hinged containers that are ubiquitous in daily life and present unique challenges for embodied interaction. These objects cannot be rep

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#19 most recent of 237 cs.CV papers we have recorded · ↑ newer: Adaptive Convolutional Sparse Coding via Information Bottleneck for Ro · ↓ older: PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Singl
Cite this page: Track, Articulate, Act: Generating Articulation from Casual Human Videos: the #19 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/track-articulate-act-generating-articulation-from-casual-human-videos.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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