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DreamView

Official PyTorch implementation of ECCV 2024 paper “DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation”.

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This repository includes the training and inference code for text-to-image generation (DreamView-2D), and text-to-3D generation (DreamView-3D).

You can check DreamView-2D/ and DreamView-3D/ for the text-to-image and text-to-3D generation codes, respectively.

News

TODO

Preliminary

Environment

Checkpoint

Text-to-image Generation

Inference

Running the below script

cd DreamView-2D
python t2i.py --num_samples 4 --fp16

and you are expected to obtain the below result: - It takes about 10G GPU memory to run the text-to-image generation, and you can modify the DreamView-2D/t2i.py to generate your own content.

Training

cd DreamView-2D
bash train.sh

Note that we use 4 8*V100 machine to train DreamView-2D by default, and to accelerate convergence, you can consider using MVDream as the initialization parameter.

Text-to-3D Generation

- Running the script below to reproduce the results shown above

cd DreamView-3D
bash reproduce.sh

Note that the above script may require ~60G GPU memory, so you may run it with an A100 GPU.

Acknowledgement

Citation

@inproceedings{yan2024DreamView,
  author = {Yan, Junkai and Gao, Yipeng and Yang, Qize and Wei, Xihan and Xie, Xuansong and Wu, Ancong and Zheng, Wei-Shi},
  title = {DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation},
  booktitle = {ECCV},
  year = {2024}
}