MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction

• URL: http://arxiv.org/abs/2603.19231v1
• Date: Thu, 19 Mar 2026 17:59:52 GMT
• Title: MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction
• Abstract summary: Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence.<n>We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture.
• Score: 47.69179070489671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi-view supervision, retrieval-based assembly, or auxiliary video generation, often sacrificing scalability or efficiency. We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. This structured reasoning process enables stable and interpretable articulation inference without external motion templates or multi-stage pipelines. Extensive experiments on PartNet-Mobility demonstrate that OM achieves state-of-the-art performance in both reconstruction accuracy and inference speed. The framework further generalizes to robotic manipulation and articulated scene reconstruction.

Related papers

• Articulated Object Reconstruction from Rest-State Observation [33.09154459685742]
  Building interactive digital twins requires recovering both 3D geometry and the kinematic structures that govern how objects articulate.<n>We introduce a rest-state formulation that reconstructs articulated objects from a single closed configuration.
  arXiv  Detail & Related papers  (2026-07-30T06:42:03Z)
• UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image [58.40343808003224]
  We present a debate-driven approach to articulated 3D object reconstruction from text or image inputs.<n>High-level agents reason about object semantics and motion using knowledge from vision-language and video models.<n>Low-level agents estimate articulation parameters and interaction points.<n>The same video prior, conditioned on the agreed articulation, then drives each part through its motion to expose occluded interiors and geometry.
  arXiv  Detail & Related papers  (2026-06-29T17:44:53Z)
• 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)
• PAct: Part-Decomposed Single-View Articulated Object Generation [45.04652409374895]
  Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR.<n>We introduce a part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation under explicit part-aware conditioning.<n>Our representation models an object as a set of movable parts, each encoded by latent tokens augmented with part identity and articulation cues.
  arXiv  Detail & Related papers  (2026-02-16T17:45:44Z)
• ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States [9.721009445297716]
  ArtGen is a conditional diffusion-based framework capable of generating articulated 3D objects with accurate geometry and coherent kinematics.<n>Specifically, ArtGen employs cross-state Monte Carlo sampling to explicitly enforce global kinematic consistency.<n>A compositional 3D-VAE latent prior enhanced with local-global attention effectively captures fine-grained geometry and global part-level relationships.
  arXiv  Detail & Related papers  (2025-12-13T17:00:03Z)
• VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video [60.63575135514847]
  Building digital twins of articulated objects from monocular video presents an essential challenge in computer vision.<n>We introduce VideoArtGS, a novel approach that reconstructs high-fidelity digital twins of articulated objects from monocular video.<n>VideoArtGS demonstrates state-of-the-art performance in articulation and mesh reconstruction, reducing the reconstruction error by about two orders of magnitude compared to existing methods.
  arXiv  Detail & Related papers  (2025-09-22T11:52:02Z)
• PAOLI: Pose-free Articulated Object Learning from Sparse-view Images [27.16160315662701]
  We present a novel framework for learning articulated object representations from sparse-view, unposed images.<n>Our approach operates with as few as four views per articulation and no camera supervision.
  arXiv  Detail & Related papers  (2025-09-04T14:51:03Z)
• GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects [4.717906057951389]
  We introduce a unified representation that jointly models geometry and motion using articulated 3D Gaussians.<n>This formulation improves robustness in motion decomposition and supports articulated objects with up to 20 parts.<n>We show that our method consistently achieves superior accuracy in part-level geometry reconstruction and motion estimation across a broad range of object types.
  arXiv  Detail & Related papers  (2025-08-20T17:59:08Z)
• EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild [79.71523320368388]
  Our work aims to reconstruct hand-object interactions from a single-view image.<n>We first design a novel pipeline to estimate the underlying hand pose and object shape.<n>With the initial reconstruction, we employ a prior-guided optimization scheme.
  arXiv  Detail & Related papers  (2024-11-21T16:33:35Z)
• Shape of Motion: 4D Reconstruction from a Single Video [42.42669078777769]
  We introduce a method for reconstructing generic dynamic scenes, featuring explicit, persistent 3D motion trajectories in the world coordinate frame.<n>First, we exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE(3) motion bases.<n>Second, we take advantage of off-the-shelf data-driven priors such as monocular depth maps and long-range 2D tracks, and devise a method to effectively consolidate these noisy supervisory signals.
  arXiv  Detail & Related papers  (2024-07-18T17:59:08Z)
• REACTO: Reconstructing Articulated Objects from a Single Video [64.89760223391573]
  We propose a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation of the joints. Our method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects.
  arXiv  Detail & Related papers  (2024-04-17T08:01:55Z)
• Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments [81.38641691636847]
  We rethink the problem of scene reconstruction from an embodied agent's perspective. We reconstruct an interactive scene using RGB-D data stream. This reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models.
  arXiv  Detail & Related papers  (2021-03-30T05:56:58Z)

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.