Summary of Genxd: Generating Any 3d and 4d Scenes, by Yuyang Zhao et al.
GenXD: Generating Any 3D and 4D Scenes
by Yuyang Zhao, Chung-Ching Lin, Kevin Lin, Zhiwen Yan, Linjie Li, Zhengyuan Yang, Jianfeng Wang, Gim Hee Lee, Lijuan Wang
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a framework called GenXD for generating 3D and 4D scenes from videos, leveraging camera and object movements. The authors develop a data curation pipeline to obtain 4D scene datasets and introduce the CamVid-30K dataset, which is used to train GenXD. The framework disentangles camera and object movements using multiview-temporal modules and employs masked latent conditions for conditioning views. Extensive evaluations are performed across various real-world and synthetic datasets, demonstrating GenXD’s effectiveness in 3D and 4D generation compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make fake 3D and 4D scenes from videos by looking at how cameras move and objects change. To do this, they first figure out how to get real-world data for these types of scenes and then create a special framework called GenXD that can learn from both 2D and 3D/4D data. This framework is good at making realistic 3D and 4D scenes that follow the camera’s path and show consistent views. |
