Our paper Luciddreamer is selected as Spotlight at CVPR 2024 and featured on Hugging Face Daily

Jun 19, 2024·
Ying-Cong Chen
Ying-Cong Chen
· 2 min read

We are thrilled to announce that our recent work, LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching, has been selected as a spotlight paper at the prestigious CVPR 2024 conference. This recognition is a testament to the innovative breakthroughs the paper introduces in the field of generative models.

LucidDreamer has been designed to transform textual descriptions directly into high-quality 3D models, overcoming previous challenges that limited the detail and accuracy of generated 3D content. The key to our approach lies in a novel method named Interval Score Matching (ISM), which ensures the generation of detailed, high-quality 3D models by effectively using pre-trained 2D diffusion models as a strong image prior.

Highlights of Our Work:

  • Innovative Technique: Our Interval Score Matching significantly enhances the text-to-3D generation process, allowing for the creation of photorealistic 3D models with unprecedented detail and fidelity.
  • Efficient and Effective: Compared to existing methods, LucidDreamer reduces training costs and complexity while delivering superior results, as evidenced by extensive experiments and qualitative assessments.
  • Community and Industry Impact: Since its feature as a daily paper on Hugging Face, our framework has attracted attention from both the academic community and industry practitioners, opening up new possibilities for creative and commercial applications.

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The code for LucidDreamer is available to the public under the EnVision-Research GitHub repository, enabling researchers and developers to build upon our work.


Authors:

  • Yixun Liang, My student from HKUST(GZ)
  • Xin Yang, My student from HKUST(GZ)
  • Jiantao Lin, My student from HKUST(GZ)
  • Haodong Li, My student from HKUST(GZ)
  • Xiaogang Xu, Collaborator from Zhejiang University
  • Ying-Cong Chen, HKUST(GZ)