datid3d-finetuned-eg3d-models

DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model

Gwanghyun Kim, Se Young Chun <br> CVPR 2023 <br> gwang-kim.github.io/datid_3d

We propose DATID-3D, a novel pipeline of text-guided domain adaptation tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain.** Unlike 3D extensions of prior text-guided domain adaptation methods, our novel pipeline was able to fine-tune the state-of-the-art 3D generator of the source domain to synthesize high resolution, multi-view consistent images in text-guided targeted domains without additional data, outperforming the existing text-guided domain adaptation methods in diversity and text-image correspondence. Furthermore, we propose and demonstrate diverse 3D image manipulations such as one-shot instance-selected adaptation and single-view manipulated 3D reconstruction to fully enjoy diversity in text.

Fine-tuned 3D generative models

Fine-tuned 3D generative models using DATID-3D pipeline are stored as *.pkl files. You can download the models in our Hugginface model pages.

Citation

@inproceedings{kim2022datid3d,
  author = {DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model},
  title = {Gwanghyun Kim and Se Young Chun},
  booktitle = {CVPR},
  year = {2023}
}

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