CC3D: Layout-Conditioned Generation of Compositional 3D Scenes-Reference-Cited by-同舟云学术

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CC3D: Layout-Conditioned Generation of Compositional 3D Scenes

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Bahmani Sherwin¹,Park Jeong Joon²,Paschalidou Despoina²,Yan Xingguang³,Wetzstein Gordon²,Guibas Leonidas²,Tagliasacchi Andrea¹

Affiliation:

1. University of Toronto

2. Stanford University

3. Simon Fraser University

Funder

Natural Sciences and Engineering Research Council of Canada

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376566.pdf?arnumber=10376566

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation;ACM Transactions on Graphics;2024-07-19

2. Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion;Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24;2024-07-13

3. RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation;2024 International Conference on 3D Vision (3DV);2024-03-18

4. P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion;Lecture Notes in Computer Science;2024

5. UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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