HyperReel

Website | Paper | Video

The code of HyperReel is available under the MIT license, as it draws from the following projects, which are also licensed under the MIT license: nerf_pl, TensoRF, and torch-ngp codebase. Licenses for all of these projects can be found in the licenses/ folder.

Table of contents

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• Installation
• Dynamic Datasets
• Static Datasets
• Running the Code on Dynamic Scenes
• Running the Code on Static Scenes
• Running the Code with Custom Parameters
• Real-Time Viewer
• Citation

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Installation

To install all required python dependences run

conda env create -f environment.yml

Note that we tested the HyperReel codebase on a machine running Ubuntu 20.04, with an NVIDIA 3090 RTX GPU, CUDA version 11.8, and 128 GB of RAM.

Dynamic Datasets

By default, we assume that:

1. All datasets are located in the ~/data folder (specified by the experiment.params.data_dir argument)
2. With the subdirectory for each individual dataset specified by the experiment.dataset.data_subdir argument (e.g., see conf/experiment/params/local.yaml and conf/experiment/dataset/technicolor.yaml).

Technicolor

Please reach out to the authors of Dataset and Pipeline for Multi-View Light-Field Video for access to the Technicolor dataset. We use the following sequences:

• Birthday (frames 150-200)
• Fabien (frames 50-100)
• Painter (frames 100-150)
• Theater (frames 50-100)
• Trains (frames 150-200)

Google Immersive

Download the Google Immersive sequences from their release page. As an example, in order to download the flames sequence, run:

wget https://storage.googleapis.com/deepview_video_raw_data/02_Flames.zip

Neural 3D Video

Download the Neural 3D video sequences from their release page. As an example, in order to download the Flame Steak sequence, run:

wget https://github.com/facebookresearch/Neural_3D_Video/releases/download/v1.0/flame_steak.zip

Static Datasets

DoNeRF

The DoNeRF dataset can be found here.

LLFF

The LLFF dataset can be found here.

Shiny

The Shiny dataset can be found here.

Spaces

The Spaces dataset can be found here.

Stanford

The Stanford dataset can be found here.

Running the Code on Dynamic Scenes

By default...

1. Checkpoints are written to the ~/checkpoints folder (specified by the experiment.params.ckpt_dir argument)
2. Logs are written to the ~/logs folder (specified bt the experiment.params.log_dir argument).

Note that it can take a few minutes to load all of the training data into memory for dynamic scenes.

Technicolor

In order to train HyperReel on a 50 frame subset of a scene from the Technicolor dataset, run:

bash scripts/run_one_technicolor.sh <gpu_to_use> <scene> <start_frame>

By default, the above command will hold-out the central camera. To train a model using all available cameras, run

bash scripts/run_one_technicolor_no_holdout.sh <gpu_to_use> <scene> <start_frame>

This will also automatically create validation images and (spiral) validation videos in the log folder for the experiment. From a trained model, you can also render a video sequence with:

bash scripts/render_one_technicolor.sh <gpu_to_use> <scene> <start_frame>

Google Immersive

In order to train HyperReel on a 50 frame subset of a scene from the Google Immersive dataset, run:

bash scripts/run_one_immersive.sh <gpu_to_use> <scene> <start_frame>

By default, the above command will hold-out the central camera. To train a model using all available cameras, run

bash scripts/run_one_immersive_no_holdout.sh <gpu_to_use> <scene> <start_frame>

Neural 3D

In order to train HyperReel on a 50 frame subset of a scene from the Neural 3D Video dataset, run:

bash scripts/run_one_n3d.sh <gpu_to_use> <scene> <start_frame>

By default, the above command will hold-out the central camera. To train a model using all available cameras, run

bash scripts/run_one_n3d_no_holdout.sh <gpu_to_use> <scene> <start_frame>

Running the Code on Static Scenes

DoNeRF

In order to train HyperReel on a scene from the DoNeRF dataset, run:

bash scripts/run_one_donerf_sphere.sh <gpu_to_use> <scene>

LLFF

In order to train HyperReel on a scene from the LLFF dataset, run:

bash scripts/run_one_llff.sh <gpu_to_use> <scene>

Shiny

In order to train HyperReel on the CD and Lab sequences from the Shiny dataset, run:

bash scripts/run_one_shiny_dense.sh <gpu_to_use> <scene>

Running the Code with Custom Parameters

The general syntax for training a model is:

python main.py experiment/dataset=<dataset_config> \
    experiment/training=<training_config> \
    experiment/model=<model_config> \
    experiment.dataset.collection=<scene_name> \
    +experiment/regularizers/tensorf=tv_4000

Where

1. <dataset_config> specifies the dataset config file, located in conf/experiment/dataset
2. <training_config> specifies the training config file, located in conf/experiment/training
3. <model_config> specifies the model config file, located in conf/experiment/model
4. <scene_name> specifies the scene name within the dataset

Regularizer syntax

The line +experiment/regularizers/tensorf=tv_4000 adds total variation and L1 regularizers on the volume tensor components, with configuration located in conf/experiment/regularizers/tensorf/tv_4000.

Real-Time Viewer

Once you have trained a HyperReel model, you can make use of the scripts/demo_* scripts in order to launch the real-time viewer.

For example, to run the real-time viewer on a scene from the technicolor dataset, run:

bash scripts/demo_technicolor.sh <gpu_to_use> <scene> <start_frame>

For static scenes, you can omit the start frame argument. For example:

bash scripts/demo_shiny_dense.sh <gpu_to_use> <scene>

Here are a couple of examples of the demo running on a workstation with a 3090 RTX GPU.

shiny_1.mov

immersive_2.mov

Citation

@article{attal2023hyperreel,
  title={HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling},
  author={Attal, Benjamin and Huang, Jia-Bin and Richardt, Christian and Zollhoefer, Michael and Kopf, Johannes and O'Toole, Matthew and Kim, Changil},
  journal={arXiv preprint arXiv:2301.02238},
  year={2022}
}