Abstract
We introduce MuVieCAST, a modular multi-view consistent style transfer network architecture that enables consistent style transfer between multiple viewpoints of the same scene. This network architecture supports both sparse and dense views, making it versatile enough to handle a wide range of multi-view image datasets. The approach consists of three modules that perform specific tasks related to style transfer, namely content preservation, image transformation, and multi-view consistency enforcement. We extensively evaluate our approach across multiple application domains including depth-map-based point cloud fusion, mesh reconstruction, and novel-view synthesis. Our experiments reveal that the proposed framework achieves an exceptional generation of stylized images, exhibiting consistent outcomes across perspectives. A user study focusing on novel-view synthesis further confirms these results, with approximately 68% of cases participants expressing a preference for our generated outputs compared to the recent state-of-the-art method. Our modular framework is extensible and can easily be integrated with various backbone architectures, making it a flexible solution for multi-view style transfer.
Original language | English |
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Title of host publication | International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT) |
Publisher | IEEE |
Pages | 1136-1145 |
Number of pages | 10 |
ISBN (Electronic) | 979-8-3503-6245-9 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 International Conference in 3D Vision - 3DV 2024 - Davos, Switzerland Duration: 18 Mar 2024 → 21 Mar 2024 https://3dvconf.github.io/2024 |
Publication series
Name | Proceedings (International Conference on 3D Vision. Online) |
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Publisher | IEEE |
ISSN (Electronic) | 2475-7888 |
Conference
Conference | 2024 International Conference in 3D Vision - 3DV 2024 |
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Abbreviated title | 3DV 2024 |
Country/Territory | Switzerland |
City | Davos |
Period | 18/03/24 → 21/03/24 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- point cloud compression
- geometry
- image transformation
- three-dimensional displays
- focusing
- estimation
- network architecture