A Comprehensive Survey on 3D Content Generation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909141170126848 |
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| author | Liu, Jian Huang, Xiaoshui Huang, Tianyu Chen, Lu Hou, Yuenan Tang, Shixiang Liu, Ziwei Ouyang, Wanli Zuo, Wangmeng Jiang, Junjun Liu, Xianming |
| author_facet | Liu, Jian Huang, Xiaoshui Huang, Tianyu Chen, Lu Hou, Yuenan Tang, Shixiang Liu, Ziwei Ouyang, Wanli Zuo, Wangmeng Jiang, Junjun Liu, Xianming |
| contents | Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is the most close visual modality to real-world 3D environment and carries enormous knowledge. The 3D content generation shows both academic and practical values while also presenting formidable technical challenges. This review aims to consolidate developments within the burgeoning domain of 3D content generation. Specifically, a new taxonomy is proposed that categorizes existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers spanning the major techniques. Besides, we discuss limitations of current 3D content generation techniques, and point out open challenges as well as promising directions for future work. Accompanied with this survey, we have established a project website where the resources on 3D content generation research are provided. The project page is available at https://github.com/hitcslj/Awesome-AIGC-3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on 3D Content Generation Liu, Jian Huang, Xiaoshui Huang, Tianyu Chen, Lu Hou, Yuenan Tang, Shixiang Liu, Ziwei Ouyang, Wanli Zuo, Wangmeng Jiang, Junjun Liu, Xianming Computer Vision and Pattern Recognition Artificial Intelligence Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is the most close visual modality to real-world 3D environment and carries enormous knowledge. The 3D content generation shows both academic and practical values while also presenting formidable technical challenges. This review aims to consolidate developments within the burgeoning domain of 3D content generation. Specifically, a new taxonomy is proposed that categorizes existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers spanning the major techniques. Besides, we discuss limitations of current 3D content generation techniques, and point out open challenges as well as promising directions for future work. Accompanied with this survey, we have established a project website where the resources on 3D content generation research are provided. The project page is available at https://github.com/hitcslj/Awesome-AIGC-3D. |
| title | A Comprehensive Survey on 3D Content Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.01166 |