arXiv Computer Vision
Sep 23

Geometric and Semantic Coupling for Interaction Understanding in 3D Scenes

The paper introduces Segment‑Snap, a method that jointly models movable parts, their motion, and the regions they operate on in 3D scenes. It uses learned predictors to identify part surfaces and handles, a geometric decoder to constrain motion with planar and upright priors, and a joint part‑and‑handle predictor to refine motion classes. Experiments on Articulate3D show that handle guidance boosts motion‑gated AP from 13.74% to 40.98%, while additional handle candidates and part‑based corrections further improve performance.

By Hanyang Kong, Xingyi Yang
arXiv AI
2d ago

PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video

arXiv:2610.00451v1 Announce Type: cross Abstract: Human motion, environmental contacts, and interaction forces are governed by common physical laws, yet existing approaches typically separate visual...

By Rikhat Akizhanov (MBZUAI), Yangsong Zhang (MBZUAI), Nikolai Kaliazin (MBZUAI), Peter Wolf (ETH Z\"urich), Yoshihiko Nakamura (MBZUAI), Pascal Fua (EPFL), Fabio Pizzati (MBZUAI), Ivan Laptev (MBZUAI)