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ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models

ViewDiff generates high-quality, multi-view consistent images of a real-world 3D object in authentic surroundings.

This is the official repository that contains source code for the CVPR 2024 paper ViewDiff.

[arXiv] [Project Page] [Video]

Teaser

If you find ViewDiff useful for your work please cite:

@inproceedings{hoellein2024viewdiff,
    title={ViewDiff: 3D-Consistent Image Generation with Text-To-Image Models},
    author={H{\"o}llein, Lukas and Bo\v{z}i\v{c}, Alja\v{z} and M{\"u}ller, Norman and Novotny, David and Tseng, Hung-Yu and Richardt, Christian and Zollh{\"o}fer, Michael and Nie{\ss}ner, Matthias},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2024}
}

Installation

Create a conda environment with all required dependencies:

conda create -n viewdiff python=3.10
conda activate viewdiff
pip install -r requirements.txt

Then install Pytorch3D by following the official instructions. For example, to install Pytorch3D on Linux (tested with Pytorch3D 0.7.4):

conda install -c fvcore -c iopath -c conda-forge fvcore iopath
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"

Then manually update triton to the required version:

pip install --upgrade triton==2.1.0

Data Preparation

<co3d_root>
<co3d_root>/teddybear
<co3d_root>/hydrant
<co3d_root>/donut
<co3d_root>/apple
...
  • Generate BLIP2 text captions from the images for each category:
export CO3DV2_DATASET_ROOT=<path/to/co3d>
python -m viewdiff.data.co3d.generate_blip2_captions --dataset-config.co3d_root <path/to/co3d> --output_file <path/to/co3d>/co3d_blip2_captions.json
  • Generate the prior preservation (aka Dreambooth) dataset for each category:
export CO3DV2_DATASET_ROOT=<path/to/co3d>
python -m viewdiff.data.co3d.generate_co3d_dreambooth_data --prompt_file <path/to/co3d>/co3d_blip2_captions.json --output_path <path/to/co3d>/dreambooth_prior_preservation_dataset
  • Recenter the poses of each object, such that the object lies within the unit cube:
export CO3DV2_DATASET_ROOT=<path/to/co3d>
python -m viewdiff.data.co3d.save_recentered_sequences --dataset-config.co3d_root <path/to/co3d>

Training

Execute the following script (requires 2x A100 80GB GPUs):

./viewdiff/scripts/train.sh <path/to/co3d> "stabilityai/stable-diffusion-2-1-base" outputs/train <category=teddybear>

If you only have a smaller GPU available and want to sanity check that everything is working, you can execute this script (e.g. on a RTX 3090 GPU):

./viewdiff/scripts/train_small.sh <path/to/co3d> "stabilityai/stable-diffusion-2-1-base" outputs/train <category=teddybear>

In our experiments, we train the model for 60K iterations.

Evaluation

First, export a trained model to a runnable checkpoint:

python -m viewdiff.convert_checkpoint_to_model --checkpoint-path <path/to/checkpoint-XXXXX>

Create 360 Degree Images In A Single Batch

Execute the following script:

./viewdiff/scripts/test/test_spherical_360_256x256.sh <path/to/co3d> <path/to/saved_model_from_checkpoint-XXXXX> outputs/single-batch-uncond-generation <num_images=10> <category=teddybear> <num_steps=50>

This creates num_images images of a single object in a single forward pass of the model (first row in the teaser image). In total num_steps objects will be created.

Create Smooth Rendering Around An Object (Elevation=60 Degrees)

Execute the following script:

./viewdiff/scripts/test/test_sliding_window_smooth_alternating_theta_60_360_256x256.sh <path/to/co3d> <path/to/saved_model_from_checkpoint-XXXXX> outputs/smooth-autoregressive-theta-60 <category=teddybear>

This creates a video rendering of an object in a spherical trajectory at 60 degrees elevation.

Create Smooth Rendering Around An Object (Elevation=30 Degrees)

Execute the following script:

./viewdiff/scripts/test/test_sliding_window_smooth_alternating_theta_30_360_256x256.sh <path/to/co3d> <path/to/saved_model_from_checkpoint-XXXXX> outputs/smooth-autoregressive-theta-30 <category=teddybear>

This creates a video rendering of an object in a spherical trajectory at 30 degrees elevation.

Condition Generation On Single Input Image

Execute the following script:

./viewdiff/scripts/test/eval_single_image_input.sh <path/to/co3d> <path/to/saved_model_from_checkpoint-XXXXX> outputs/single-image-eval <category=teddybear>

This renders novel views for an object of the test set given a single image input. We also save the quantitative metrics PSNR, SSIM, LPIPS in the output directory.

Optimize a NeRF

We provide an easy way to train a NeRF from our generated images. When creating a smooth rendering, we save a transforms.json file in the standard NeRF convention, that can be used to optimize a NeRF for the generated object. It can be used with standard NeRF frameworks like Instant-NGP or NeRFStudio.

LICENSE

The majority of this repository is licensed under CC-BY-NC, however portions of the project are available under separate license terms:

  • diffusers is licensed under the Apache 2.0 license. We use the repository to extend the default U-Net architecture, by adapting the model definition found in the original library.

  • Fastplane (aka Lightplane) module for memory-efficient volumetric rendering is licensed under the BSD License. Please consider citing the original paper if you find the module useful:

@article{cao2024lightplane,
    author = {Ang Cao and Justin Johnson and Andrea Vedaldi and David Novotny},
    title = {Lightplane: Highly-Scalable Components for Neural 3D Fields},
    journal = {arXiv},
    year = {2024},
}