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.
Original language | English |
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Title of host publication | Proceedings : SIGGRAPH 2024 Conference Papers |
Editors | Andres BURBANO, Denis ZORIN, Wojciech JAROSZ |
Publisher | Association for Computing Machinery, Inc |
Number of pages | 12 |
ISBN (Electronic) | 9798400705250 |
DOIs | |
Publication status | Published - 13 Jul 2024 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2024 Owner/Author.
Funding
This work is supported by Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 14201921), and Natural Sciences and Engineering Research Council of Canada (Project No. 611370).
Keywords
- 3D shape editing
- shape representation