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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.12928 |
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| _version_ | 1866913550185791488 |
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| author | Sun, Jingxiang Peng, Cheng Shao, Ruizhi Guo, Yuan-Chen Zhao, Xiaochen Li, Yangguang Cao, Yanpei Zhang, Bo Liu, Yebin |
| author_facet | Sun, Jingxiang Peng, Cheng Shao, Ruizhi Guo, Yuan-Chen Zhao, Xiaochen Li, Yangguang Cao, Yanpei Zhang, Bo Liu, Yebin |
| contents | We introduce DreamCraft3D++, an extension of DreamCraft3D that enables efficient high-quality generation of complex 3D assets. DreamCraft3D++ inherits the multi-stage generation process of DreamCraft3D, but replaces the time-consuming geometry sculpting optimization with a feed-forward multi-plane based reconstruction model, speeding up the process by 1000x. For texture refinement, we propose a training-free IP-Adapter module that is conditioned on the enhanced multi-view images to enhance texture and geometry consistency, providing a 4x faster alternative to DreamCraft3D's DreamBooth fine-tuning. Experiments on diverse datasets demonstrate DreamCraft3D++'s ability to generate creative 3D assets with intricate geometry and realistic 360° textures, outperforming state-of-the-art image-to-3D methods in quality and speed. The full implementation will be open-sourced to enable new possibilities in 3D content creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model Sun, Jingxiang Peng, Cheng Shao, Ruizhi Guo, Yuan-Chen Zhao, Xiaochen Li, Yangguang Cao, Yanpei Zhang, Bo Liu, Yebin Computer Vision and Pattern Recognition We introduce DreamCraft3D++, an extension of DreamCraft3D that enables efficient high-quality generation of complex 3D assets. DreamCraft3D++ inherits the multi-stage generation process of DreamCraft3D, but replaces the time-consuming geometry sculpting optimization with a feed-forward multi-plane based reconstruction model, speeding up the process by 1000x. For texture refinement, we propose a training-free IP-Adapter module that is conditioned on the enhanced multi-view images to enhance texture and geometry consistency, providing a 4x faster alternative to DreamCraft3D's DreamBooth fine-tuning. Experiments on diverse datasets demonstrate DreamCraft3D++'s ability to generate creative 3D assets with intricate geometry and realistic 360° textures, outperforming state-of-the-art image-to-3D methods in quality and speed. The full implementation will be open-sourced to enable new possibilities in 3D content creation. |
| title | DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.12928 |