mlsanityData to 7 Oct 2026

SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation

arXiv 2201.13168 ↗First posted 31 Jan 2022cs.GR cs.CV cs.LGcited by 28

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Abstract (arXiv)

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques. However, adopting them for 3D shape modeling and editing is challenging. We introduce a method for Editing Implicit Shapes Through Part Aware GeneraTion, permuted in short as SPAGHETTI. Our architecture allows for manipulation of implicit shapes by means of transforming, interpolating and combining shape segments together, without requiring explicit part supervision. SPAGHETTI disentangles shape part representation into extrinsic and intrinsic geometric information. This characteristic enables a generative framework with part-level control. The modeling capabilities of SPAGHETTI are demonstrated using an interactive graphical interface, where users can directly edit neural implicit shapes.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 57 of its 103 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

57 of 103 references.

References · 57

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38 more
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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 8; 2024: 9; 2025: 5; 2026: 4.

28 papers.

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  1. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  2. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  3. 2024Part123: Part-aware 3D Reconstruction from a Single-view Imagecited by 54
  4. 2023PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervisioncited by 36
  5. 2023Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulationcited by 27
  6. 2025OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generationcited by 27
  7. 2024UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusioncited by 23
  8. 2023Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapescited by 14
  9. 2024PASTA: Controllable Part-Aware Shape Generation with Autoregressive Transformerscited by 14
  10. 2023SENS: Part-Aware Sketch-based Implicit Neural Shape Modelingcited by 8
  11. 2023Advances in 3D Neural Stylization: A Surveycited by 8
  12. 2025StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidancecited by 8
16 more
  1. 2023Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attentioncited by 7
  2. 2025TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimizationcited by 6
  3. 2026PEGAsus: 3D Personalization of Geometry and Appearancecited by 5
  4. 2023Adaptive Local Basis Functions for Shape Completioncited by 4
  5. 2024Compositional Neural Texturescited by 4
  6. 2025SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generationcited by 4
  7. 2024Occupancy-Based Dual Contouringcited by 3
  8. 2026Deep Sketch-Based 3D Modeling: A Surveycited by 3
  9. 2024Controllable Shape Modeling with Neural Generalized Cylindercited by 2
  10. 2024JADE: Joint-aware Latent Diffusion for 3D Human Generative Modelingcited by 2
  11. 2026BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularitycited by 1
  12. 2022Learning Latent Part-Whole Hierarchies for Point Cloudscited by 0
  13. 2024Diverse Part Synthesis for 3D Shape Creationcited by 0
  14. 2024Improved Convex Decomposition with Ensembling and Negative Primitivescited by 0
  15. 2025Hierarchical Transformers for Unsupervised 3D Shape Abstractioncited by 0
  16. 2026Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generationcited by 0