You're currently viewing an old version of this dataset. To see the current version, click here.

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

This dataset has no data

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

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

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