G3DR: generative 3D reconstruction in ImageNet
File(s)
Author(s)
Reddy, Pradyumna
Elezi, Ismail
Deng, Jiankang
Type
Conference Paper
Abstract
We introduce a novel 3D generative method, Generative 3D Reconstruction (G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects from single images, addressing the limitations of existing methods. At the heart of our framework is a novel depth reg-ularization technique that enables the generation of scenes with high-geometric fidelity. G3DR also leverages a pre-trained language-vision model, such as CLIP, to enable reconstruction in novel views and improve the visual realism of generations. Additionally, G3DR designs a simple but effective sampling procedure to further improve the quality of generations. G3DR offers diverse and efficient 3D asset generation based on class or text conditioning. Despite its simplicity, G3DR is able to beat state-of-the-art methods, improving over them by up to 22% in per-ceptual metrics and 90% in geometry scores, while needing only half of the training time. Code is available at https://github.com/preddy5/G3DR.
Date Issued
2024-09-16
Date Acceptance
2024-06-01
Citation
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp.9655-9665
ISSN
1063-6919
Publisher
IEEE
Start Page
9655
End Page
9665
Journal / Book Title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Copyright Statement
© 2024 IEEE. This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.
Source
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publication Status
Published
Start Date
2024-06-16
Finish Date
2024-06-22
Coverage Spatial
Seattle, WA, USA