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CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization

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Chi-Wing FuAuthors Info & Claims

SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers

Article No.: 110, Pages 1 - 12

https://doi.org/10.1145/3641519.3657412

Published: 13 July 2024 Publication History

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Abstract

This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space. This work has three innovations. First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing. This representation includes a latent code, which captures high-level global semantics of the shape, and a 3D neural feature volume, which provides a spatial context to associate with the local shape changes given by the editing. Second, we formulate the coupled neural shape optimization procedure to co-optimize the two coupled components in the representation subject to the editing operation. Last, we offer various 3D shape editing operators, i.e., copy, resize, delete, and drag, and derive each into an objective for guiding the CNS optimization, such that we can iteratively co-optimize the latent code and neural feature volume to match the editing target. With our approach, we can achieve a rich variety of editing results that are not only aware of the shape semantics but are also not easy to achieve by existing approaches. Both quantitative and qualitative evaluations demonstrate the strong capabilities of our approach over the state-of-the-art solutions.

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Cited By

Li JZhang JChen F(2024)iShapEditing: Intelligent Shape Editing with Diffusion ModelsComputer Graphics Forum10.1111/cgf.1525343:7Online publication date: 8-Nov-2024
https://doi.org/10.1111/cgf.15253
Deng YWang RZhang YTai YTang C(2024)DragVideo: Interactive Drag-Style Video EditingComputer Vision – ECCV 202410.1007/978-3-031-72992-8_11(183-199)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-3-031-72992-8_11

Index Terms

CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Mesh models
      2. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers

July 2024

1106 pages

ISBN:9798400705250

DOI:10.1145/3641519

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Cited By

Li JZhang JChen F(2024)iShapEditing: Intelligent Shape Editing with Diffusion ModelsComputer Graphics Forum10.1111/cgf.1525343:7Online publication date: 8-Nov-2024
https://doi.org/10.1111/cgf.15253
Deng YWang RZhang YTai YTang C(2024)DragVideo: Interactive Drag-Style Video EditingComputer Vision – ECCV 202410.1007/978-3-031-72992-8_11(183-199)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-3-031-72992-8_11

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