arXiv Computer Vision
PAOLI: Pose-free Articulated Object Learning from Sparse-view Images
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 11
Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning
Artic-O is an end‑to‑end, feed‑forward framework that reconstructs articulated objects from sparse images by learning latent geometry. It maps multi‑state observations into a pretrained latent geometry space, uses a frozen flow‑matching decoder for complete‑shape priors, and fuses visual tokens with geometry latents in an image‑grounded part‑reasoning module to segment active parts and predict articulation. Trained with a geometry‑to‑articulation curriculum and a decoupled two‑pass strategy, Artic‑O achieves high reconstruction quality and articulation accuracy while drastically reducing inference time from 9 minutes to about 0.3 seconds per object.
arXiv AI
Sep 18
FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
FAMOS is a feed‑forward model that predicts movable‑part segmentation and joint parameters from a sparse, unordered set of partial point clouds. It jointly reasons over multiple observations using a Multi‑state Articulation Transformer that alternates state‑wise and global attention, and introduces an observed articulation span objective to supervise motion ranges across inputs. A procedural data generator supplies self‑annotated assets for training, and experiments on PartNet‑Mobility, ACD, and ArtiCraft‑10K show consistent improvements over existing feed‑forward and optimization‑based baselines.
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 Computer Vision
3d 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 AI
1d 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...
Hugging Face Trending Papers
Jul 2
PWM-ArtGen: Part World Model for Articulated Object Generation
The key challenge in articulated 3D object generation from a single image is accurately predicting the underlying kinematic structure. Existing methods either infer kinematic parameters directly from a static image that lacks dynamic part-level kinematic relationships, or estimate parameters from visual dynamics generated from a single image, which is prone to accumulated errors of two steps.