Retrieval-Augmented Score Distillation for Text-to-3D Generation

Text-to-3D generation has emerged as an important application that enables non-experts to easily create 3D contents. The conventional approaches for text-to-3D train a generative model directly on 3D data from scratch.

Data and Resources

Cite this as

Junyoung Seo, Susung Hong, Wooseok Jang, In`es Hyeonsu Kim, Minseop Kwak, Doyup Lee, Seungryong Kim (2024). Dataset: Retrieval-Augmented Score Distillation for Text-to-3D Generation. https://doi.org/10.57702/h30rttwd

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2402.02972
Author Junyoung Seo
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Susung Hong
Wooseok Jang
In`es Hyeonsu Kim
Minseop Kwak
Doyup Lee
Seungryong Kim
Homepage https://ku-cvlab.github.io/ReDream/