Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects

• URL: http://arxiv.org/abs/2605.24403v1
• Date: Sat, 23 May 2026 05:19:05 GMT
• Title: Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
• Abstract summary: Artiverse contains 5.4K human-authored objects across a broad range of 88 categories.<n>Artiverse provides a data resource to advance functional understanding for articulated objects.
• Score: 32.16757152704498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Artiverse, a diverse and physically grounded dataset of high-quality articulated 3D objects designed for realistic functional modeling and simulation. Artiverse contains 5.4K human-authored objects across a broad range of 88 categories, aggregated from multiple 3D static repositories. Objects are annotated with functional parts, interior structures, realistic kinematic relationships and articulated joints including multi-DoF joints, and physical attributes such as metric scale, material, and mass. We develop a semi-automated annotation pipeline that combines few-shot segmentation, geometric reasoning, and multi-stage human verification to achieve high-quality and efficient annotation, reducing manual annotation time by over 30%. We demonstrate the value of Artiverse on tasks of part mobility analysis, articulated object generation, and physics-based interaction. Artiverse provides a data resource to advance functional understanding for articulated objects.

Related papers

• Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control [86.64250947574749]
  Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations.<n>Recent neural networks can estimate the articulated structure of 3D objects, but their generalization remains limited by the scarcity of annotated data.<n>We introduce Instruct-Particulate, a model that takes a 3D mesh together with a target kinematic specification.
  arXiv  Detail & Related papers  (2026-06-12T17:59:36Z)
• ArtLLM: Generating Articulated Assets via 3D LLM [19.814132638278547]
  ArtLLM is a novel framework for generating high-quality articulated assets directly from complete 3D meshes.<n>At its core is a 3D multimodal large language model trained on a large-scale articulation dataset.<n> Experiments show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction.
  arXiv  Detail & Related papers  (2026-03-01T15:07:46Z)
• Articulated 3D Scene Graphs for Open-World Mobile Manipulation [55.97942733699124]
  We present MoMa-SG, a framework for building semantic-kinematic 3D scene graphs of articulated scenes.<n>We estimate articulation models using a novel unified twist estimation formulation.<n>We also introduce the novel Arti4D-Semantic dataset.
  arXiv  Detail & Related papers  (2026-02-18T10:40:35Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction.<n>We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images.<n>We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision.<n>Existing methods often fail to effectively integrate information across different object states.<n>We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
  arXiv  Detail & Related papers  (2025-02-26T10:25:32Z)
• Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling [48.78204955169967]
  Articulate Anymesh is an automated framework that is able to convert rigid 3D mesh into its articulated counterpart in an open-vocabulary manner.<n>Our experiments show that Articulate Anymesh can generate large-scale, high-quality 3D articulated objects, including tools, toys, mechanical devices, and vehicles.
  arXiv  Detail & Related papers  (2025-02-04T18:59:55Z)
• ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors [73.26004792375556]
  This paper shows that robustness and generalisation to novel scene objects in 3D object-aware character synthesis can be achieved by training a motion model with as few as one reference object. We leverage an implicit feature representation trained on object-only datasets, which encodes an SE(3)-equivariant descriptor field around the object. We demonstrate substantial improvements in 3D virtual character motion and interaction quality and robustness to scenarios with unseen objects.
  arXiv  Detail & Related papers  (2023-08-24T17:59:51Z)
• Full-Body Articulated Human-Object Interaction [61.01135739641217]
  CHAIRS is a large-scale motion-captured f-AHOI dataset consisting of 16.2 hours of versatile interactions. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process. By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation.
  arXiv  Detail & Related papers  (2022-12-20T19:50:54Z)
• AKB-48: A Real-World Articulated Object Knowledge Base [38.4899076076656]
  We present AKB-48: a large-scale Articulated object Knowledge Base which consists of 2,037 real-world 3D articulated object models of 48 categories. To build the AKB-48, we present a fast articulation knowledge modeling (FArM) pipeline, which can fulfill the ArtiKG for an articulated object within 10-15 minutes. Using our dataset, we propose AKBNet, a novel integral pipeline for Category-level Visual Articulation Manipulation (C-VAM) task.
  arXiv  Detail & Related papers  (2022-02-17T03:24:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.