Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure

• URL: http://arxiv.org/abs/2605.10438v1
• Date: Mon, 11 May 2026 12:09:28 GMT
• Title: Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure
• Abstract summary: Current 3D tokenizers treat representation as spatial compression, but leave component ownership and attachment validity implicit.<n>We formulate an alternative view, interface-centric generative states, in which tokenization constructs an operational state rather than a passive compressed code.<n>We instantiate this formulation with Component-Conditioned Canonical Local Tokens (C2LT-3D), factorizing representation into canonical local geometry, partition-conditioned context, and relational seam variables.
• Score: 55.17771962316751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current 3D tokenizers largely treat representation as spatial compression: compact codes reconstruct surface geometry, but leave component ownership and attachment validity implicit. In open-world assets with intersecting components, noisy topology, and weak canonical structure, this creates a representation mismatch: local shape, component identity, and assembly relations become entangled in a latent stream and are not natively addressable during decoding. We formulate an alternative view, interface-centric generative states, in which tokenization constructs an operational state rather than a passive compressed code. The state exposes local geometry, component ownership, and attachment validity as variables that can be queried, constrained, and repaired during decoding. We instantiate this formulation with Component-Conditioned Canonical Local Tokens (C2LT-3D), factorizing representation into canonical local geometry, partition-conditioned context, and relational seam variables. Each factor targets a distinct failure mode of compression-centric tokens: pose leakage, cross-component interference, or invalid local attachment. This exposed state supports attachment validation, latent structural repair, targeted intervention, and constrained serialization without a separate post-hoc structure recovery module. Trained on single-object CAD models and evaluated zero-shot on open-world multi-component assets, C2LT-3D improves structural robustness and shows that its latent variables remain actionable under adversarial attachment settings. These results suggest that open-world 3D generative representations should be evaluated not only by reconstruction fidelity, but by whether their discrete states remain operational for assembly-level structural reasoning.

Related papers

• USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes [57.4583999658488]
  Spatial representation learning for autonomous driving aims to map raw visual signals into structured 3D scene representations.<n>Existing methods treat dynamic reconstruction and instance-level perception as separate tasks.<n>We propose USR-Drive, a unified conditional generative framework that recovers dense dynamic geometry and instance-level object layouts within a shared scene representation.
  arXiv  Detail & Related papers  (2026-08-19T15:29:06Z)
• SCULPT: Subtractive Composition for 3D Part Generation [48.947695076233835]
  Part-aware 3D generation aims to create digital assets that are coherent as complete objects.<n>We propose SCULPT, a framework that addresses these challenges through subtractive composition.
  arXiv  Detail & Related papers  (2026-08-13T17:55:04Z)
• GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images [61.2170105836525]
  We propose an RGB-only framework that reformulates reconstruction as physically-grounded scene factorization and outputs a structured hybrid scene representation.<n>The resulting representation combines explicit rigid bodies with a decoupled background, enabling direct physics simulation while preserving visual realism.
  arXiv  Detail & Related papers  (2026-06-02T17:13:01Z)
• Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views [0.8435195043404353]
  We introduce Generative Reconstruction from Disjoint Views as a new paradigm.<n>We propose GLADOS, a general, modular framework that operates through three stages.<n>As an architectureagnostic framework, GLADOS enables seamless integration of future advances in generation, reconstruction, and inpainting.
  arXiv  Detail & Related papers  (2026-05-08T10:24:23Z)
• Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration [41.34371852767098]
  CEA is a token-wise dynamic parameterization framework for all-in-one image restoration.<n>CEA improves average restoration quality over strong prompt-, descriptor-, and expert-based baselines.
  arXiv  Detail & Related papers  (2026-05-07T12:31:24Z)
• LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting [77.81227097905865]
  Anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models.<n>We propose a Local Geometry-aware Hierarchical Context Compression framework for 3DGS that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation.<n>Experiments show that LG-HCC effectively alleviates structural preservation issues,achieving superior geometric integrity and rendering fidelity while reducing storage by up to 30.85x compared to the Scaffold-GS baseline on the Mip-NeRF360 dataset.
  arXiv  Detail & Related papers  (2026-03-30T13:39:35Z)
• SARe: Structure-Aware Large-Scale 3D Fragment Reassembly [14.2680020647518]
  3D fragment reassembly aims to recover the rigid poses of unordered fragment point clouds or meshes in a common object coordinate system.<n>Existing end-to-end approaches are prone to cascading failures due to unreliable contact reasoning.<n>We propose Structure-Aware Reassembly (SARe), a generative framework with SARe-Gen for Euclidean-space assembly generation and SARe-Refine for inference-time refinement.
  arXiv  Detail & Related papers  (2026-03-23T06:13:14Z)
• CEI-3D: Collaborative Explicit-Implicit 3D Reconstruction for Realistic and Fine-Grained Object Editing [51.73433734209541]
  Existing 3D editing methods often produce unrealistic and unrefined results due to the deeply integrated nature of their reconstruction networks.<n>This paper introduces CEI-3D, an editing-oriented reconstruction pipeline designed to facilitate realistic and fine-grained editing.<n>Our approach achieves more realistic and fine-grained editing results than the state-of-the-art (SOTA) methods while requiring less editing time.
  arXiv  Detail & Related papers  (2026-03-12T11:15:11Z)
• StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation [57.06461272772509]
  StdGEN++ is a novel and comprehensive system for generating high-fidelity, semantically decomposed 3D characters from diverse inputs.<n>It achieves state-of-the-art performance, significantly outperforming existing methods in geometric accuracy and semantic disentanglement.<n>The resulting structural independence unlocks advanced downstream capabilities, including non-destructive editing, physics-compliant animation, and gaze tracking.
  arXiv  Detail & Related papers  (2026-01-12T15:41:27Z)
• LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning [26.88556500272625]
  We propose a novel 3D variational autoencoder framework built upon unsigned distance fields (UDFs)<n>Our core innovation is a local-to-global architecture that processes the UDF by partitioning it into uniform subvolumes, UBlocks.<n> Experiments demonstrate state-of-the-art performance in both reconstruction accuracy and generative quality, yielding superior surface smoothness and geometric flexibility.
  arXiv  Detail & Related papers  (2025-11-13T07:34:43Z)
• Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes [60.92139345612904]
  We present Light-SQ, a novel superquadric-based optimization framework.<n>We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition.<n>Experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics.
  arXiv  Detail & Related papers  (2025-09-29T16:18:32Z)
• Unsupervised Learning of Intrinsic Structural Representation Points [50.92621061405056]
  Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points.
  arXiv  Detail & Related papers  (2020-03-03T17:40:00Z)

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.