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SPAGHETTI: editing implicit shapes through part aware generation

Authors:

Olga Sorkine-Hornung,

Daniel Cohen-OrAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 4

Article No.: 106, Pages 1 - 20

https://doi.org/10.1145/3528223.3530084

Published: 22 July 2022 Publication History

Abstract

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. Our code, editing user interface demo and pre-trained models are available at github.com/amirhertz/spaghetti.

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Cited By

Liu ALin CLiu YLong XDou ZGuo HLuo PWang W(2024)Part123: Part-aware 3D Reconstruction from a Single-view ImageACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657482(1-12)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657482
Sella EFiebelman GAtia NAverbuch-Elor H(2024)Spice·E: Structural Priors in 3D Diffusion using Cross-Entity AttentionACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657461(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657461
Kusupati UGaillard MThiery JKaiser A(2024)Semantic Shape Editing with Parametric Implicit TemplatesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657421(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657421
Show More Cited By

Index Terms

SPAGHETTI: editing implicit shapes through part aware generation
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
    2. Shape modeling
  2. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 4

July 2022

1978 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3528223

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Published: 22 July 2022

Published in TOG Volume 41, Issue 4

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Cited By

Liu ALin CLiu YLong XDou ZGuo HLuo PWang W(2024)Part123: Part-aware 3D Reconstruction from a Single-view ImageACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657482(1-12)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657482
Sella EFiebelman GAtia NAverbuch-Elor H(2024)Spice·E: Structural Priors in 3D Diffusion using Cross-Entity AttentionACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657461(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657461
Kusupati UGaillard MThiery JKaiser A(2024)Semantic Shape Editing with Parametric Implicit TemplatesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657421(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657421
Hu JHui KLiu ZLi RFu C(2024)Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and ManipulationACM Transactions on Graphics10.1145/363530443:2(1-18)Online publication date: 3-Jan-2024
https://dl.acm.org/doi/10.1145/3635304
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