arXiv AI
FACT: Fidelity-Aware Construction of Articulated Twins
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
4d ago
FactorizedHMR: A Hybrid Framework for Video Human Mesh Recovery
arXiv:2605.14854v3 Announce Type: replace-cross Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evide...
arXiv AI
Jun 16
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
arXiv:2606. 16202v1 Announce Type: cross Abstract: Humans naturally understand object physics through everyday interactions, but faithfully predicting complex deformable dynamics, such as elastic materials and fabrics, remains a major challenge for computer vision and robotics.
arXiv Computer Vision
6d ago
Track2Art: Articulated Object Model Recovery with Visual-Geometric Track Representations
arXiv:2609.27675v2 Announce Type: replace Abstract: Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinemat...
arXiv Computer Vision
Sep 24
Track2Art: Motion-Centric Articulated Object Model Recovery from 2D Point Trackers
Track2Art is a motion‑centric framework that recovers articulated object models from RGB‑D interaction videos by lifting 2D point tracks into 3D trajectories. It groups these trajectories into rigid‑part hypotheses and uses learned‑analytic reasoning to infer directed kinematic relations, joint types, and joint geometry. On the PartNet‑Mobility benchmark, it achieves 0.695 Point IoU and 0.410 end‑to‑end J@20 without requiring ground‑truth part counts or test‑time optimization.
arXiv AI
Aug 25
Operational digital twin clinics enable task-based evaluation of embodied AI
arXiv:2608.21416v1 Announce Type: cross Abstract: Embodied artificial intelligence (AI) must be tested in the clinical environments where it will operate, but building realistic, robot-testable setti...
arXiv AI
Sep 21
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
arXiv:2609.21751v1 Announce Type: cross Abstract: Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these...