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Official implementation of DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model
Jingxiang Sun, Cheng Peng, Ruizhi Shao, Yuanchen Guo, Xiaochen Zhao, Yangguang Li, Yanpei Cao, Bo Zhang, Yebin Liu
Abstract: 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.
This code is built on the amazing open-source projects threestudio-project and stable-dreamfusion.
@article{sun2024dreamcraft3d++,
title={DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model},
author={Sun, Jingxiang and Peng, Cheng and Shao, Ruizhi and Guo, Yuan-Chen and Zhao, Xiaochen and Li, Yangguang and Cao, Yanpei and Zhang, Bo and Liu, Yebin},
journal={arXiv preprint arXiv:2410.12928},
year={2024}
}
@article{sun2023dreamcraft3d,
title={Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior},
author={Sun, Jingxiang and Zhang, Bo and Shao, Ruizhi and Wang, Lizhen and Liu, Wen and Xie, Zhenda and Liu, Yebin},
journal={arXiv preprint arXiv:2310.16818},
year={2023}
}