arXiv AI

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 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.

By Xuyang Wang, Zhenyu Li, Jian Ding, Habib Slim, Peter Wonka, Hongdong Li, Mohamed Elhoseiny

computer-vision

arXiv Computer Vision

Sep 22

PAOLI: Pose-free Articulated Object Learning from Sparse-view Images

arXiv:2509.04276v3 Announce Type: replace Abstract: We present a method for modeling articulated objects from sparse images with unknown camera poses. Existing approaches require dense multi-view obs...

By Jianning Deng, Kartic Subr, Hakan Bilen

computer-vision benchmarks

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.

diffusion

arXiv Computer Vision

Sep 17

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

arXiv:2609.19142v1 Announce Type: new Abstract: World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse...

By Bardienus P. Duisterhof, Kaifeng Zhang, Adam Hung, Bowen Wen, Stan Birchfield, Yunzhu Li, Deva Ramanan, Jeffrey Ichnowski

llms robotics fine-tuning benchmarks

Hugging Face Trending Papers

Sep 16

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into do...

llms robotics fine-tuning benchmarks

arXiv Computer Vision

Sep 23

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction

arXiv:2609.23796v2 Announce Type: replace Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open ch...

By Yang-Tian Sun, Tianjia Liu, Zehuan Huang, Yi-Hua Huang, Xiaoyang Lyu, Ziyi Yang, Zi-Xin Zou, Yuan-Chen Guo, Yan-Pei Cao, Xiaojuan Qi

llms diffusion multimodal safety

arXiv Computer Vision

Sep 22

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene

arXiv:2609.23796v1 Announce Type: new Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challe...

By Yang-Tian Sun, Tianjia Liu, Zehuan Huang, Yi-Hua Huang, Xiaoyang Lyu, Ziyi Yang, Zi-Xin Zou, Yuan-Chen Guo, Yan-Pei Cao, Xiaojuan Qi

llms diffusion multimodal safety

arXiv Computer Vision

Sep 14

UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction

arXiv:2609.12898v1 Announce Type: new Abstract: Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized b...

By Xinqiang Yu, Zekun qi, Jiawei He, Wenyao Zhang, Xuchuan Chen, Guaocai Yao, Li Yi, Zhaoxiang Zhang, He Wang

llms rag computer-vision robotics fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jul 13

HandFlow: Fully Generative 4D Hand Recovery with Flow Matching

Accurate monocular 4D hand reconstruction remains challenging. Per-frame discriminative regressors lack temporal context and often produce jittery predictions.

llms diffusion benchmarks

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...

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

robotics

arXiv AI

Jul 14

HandFlow: Fully Generative 4D Hand Recovery with Flow Matching

arXiv:2607. 11221v1 Announce Type: cross Abstract: Accurate monocular 4D hand reconstruction remains challenging.

By Mingxi Xu, Bowen Duan, Yi Gu, Zhengyang Shen, Renjing Xu, Yutao Yue

llms diffusion benchmarks

arXiv AI

Sep 24

MessyKitchens: Contact-rich object-level 3D scene reconstruction

MessyKitchens introduces a new dataset of cluttered real-world kitchen scenes with detailed 3D object shapes, poses, and accurate contact information. The authors extend the SAM 3D single-object reconstruction method with a Multi-Object Decoder (MOD) to jointly reconstruct entire scenes, achieving better registration accuracy and reduced inter-object penetration compared to prior work. The dataset, benchmark, code, and pretrained models will be publicly released on the project website.

By Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati, Ivan Laptev

computer-vision robotics benchmarks

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