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Partcrafter: Structured 3d mesh generation via compositional latent diffusion trans- formers

22 Pith papers cite this work. Polarity classification is still indexing.

22 Pith papers citing it
abstract

We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce monolithic 3D shapes or follow two-stage pipelines, i.e., first segmenting an image and then reconstructing each segment, PartCrafter adopts a unified, compositional generation architecture that does not rely on pre-segmented inputs. Conditioned on a single image, it simultaneously denoises multiple 3D parts, enabling end-to-end part-aware generation of both individual objects and complex multi-object scenes. PartCrafter builds upon a pretrained 3D mesh diffusion transformer (DiT) trained on whole objects, inheriting the pretrained weights, encoder, and decoder, and introduces two key innovations: (1) A compositional latent space, where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts, ensuring global coherence while preserving part-level detail during generation. To support part-level supervision, we curate a new dataset by mining part-level annotations from large-scale 3D object datasets. Experiments show that PartCrafter outperforms existing approaches in generating decomposable 3D meshes, including parts that are not directly visible in input images, demonstrating the strength of part-aware generative priors for 3D understanding and synthesis. Code and training data will be released.

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cs.CV 19 cs.GR 3

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2026 19 2025 3

representative citing papers

UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image

cs.CV · 2026-06-29 · unverdicted · novelty 7.0 · 2 refs

UnfoldArt uses a two-round structured debate between high-level semantic agents and low-level parameter agents, grounded in generated video, to infer articulation and reconstruct full articulated 3D objects including occluded geometry from text or image inputs.

3D-PLOT-LLM: Part-Level Object Tokens for 3D Large Language Models

cs.CV · 2026-06-18 · unverdicted · novelty 7.0

By inserting per-region markers and reserved vocabulary tokens before frozen encoder patches and refining them via MSR, 3D-PLOT-LLM adds part-level addressing to 3D LLMs, outperforming baselines on PartVerse-QA and 3DCoMPaT-GrIn with minimal new parameters.

3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image

cs.CV · 2026-04-06 · unverdicted · novelty 7.0

3D-Fixer performs in-place 3D asset completion from single-view partial point clouds via coarse-to-fine generation with ORFA conditioning, plus a new ARSG-110K dataset, to achieve higher geometric accuracy than MIDI and Gen3DSR while keeping diffusion efficiency.

ART: Articulated Reconstruction Transformer

cs.CV · 2025-12-16 · unverdicted · novelty 7.0

ART is a category-agnostic transformer that maps sparse multi-state RGB images to per-part 3D geometry, texture, and articulation parameters via learnable part slots.

Fishbone: From One 3D Asset to a Million Controllable Edits

cs.CV · 2026-05-24 · unverdicted · novelty 6.0

Fishbone introduces a unified rib-spine representation computed via adaptive heat method, iso-contour ribs, and geometry-aware spine that enables real-time parametric deformation, reduced-space simulation, and animation on general meshes.

Pixal3D: Pixel-Aligned 3D Generation from Images

cs.CV · 2026-05-11 · unverdicted · novelty 6.0

Pixal3D performs pixel-aligned 3D generation from images via back-projected multi-scale feature volumes, achieving fidelity close to reconstruction while supporting multi-view and scene synthesis.

FurnSet: Exploiting Repeats for 3D Scene Reconstruction

cs.CV · 2026-04-22 · unverdicted · novelty 6.0

FurnSet improves single-view 3D scene reconstruction by using per-object CLS tokens and set-aware self-attention to group and jointly reconstruct repeated object instances, with added scene-object conditioning and layout optimization.

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Showing 22 of 22 citing papers.