arXiv Computer Vision
Sep 18

FunArt: Decoding Functional Structure and Articulation from Generative 3D Latents

FunArt is a framework that builds articulation‑aware functional 3D scene graphs from a single static RGB‑D observation. It reconstructs object instances, converts their geometry into the O‑Voxel representation of TRELLIS.2, and uses a frozen sparse‑compression VAE as a structural prior. A lightweight query‑based decoder jointly segments movable parts and functional interactive elements while estimating motion type, axis, origin, and range, achieving state‑of‑the‑art results on the Articulate3D dataset.

By Dennis Rotondi, Abdelrhman Werby, Kai O. Arras
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.

By Xiaotong Li, Yixiong Jing, Junsheng Ding, Weihang Li, Benjamin Busam, Guangming Wang, Brian Sheil