Deep Flow Rendering

This is the original implementation for the Computer Graphics Forum (2022) paper:
"Deep Flow Rendering: View Synthesis via Layer-aware Reflection Flow",
by Pinxuan Dai & Ning Xie, UESTC.

• Open-access paper is avaliable at Eurographics Digital Library.
• Oral recording at EGSR 2022 and supplementary video are here.

Reqiurments

TensorFlow 1.15.0, NVdiffrast 0.3.0, and install other packages via:

conda env create -f requirements.yml
conda activate dfr

Usage

• Clone this repository and prepare test data as below.
• Specify data path, model name, and training configurations directly in code/main.py.
• Run:

cd dfr/code
python main.py

Data

Example data:

• Download example data used in the paper from here.
• Unzip it in the dfr base dir:

mv path_to_download/dfr_data.zip ./
unzip dfr_data.zip 

Custome data:

• Use COLMAP's:
  • Sparse reconstruction for camera poses (use pinhole model and txt output) to get cameras.txt and images.txt,
  • Dense reconstruction for mesh (might need manual configuration for a fine mesh) and convert it into .obj format.
• Use Blender (or any other equivalent like xatlas) to generate texture atlas for the reconstructed mesh.obj.
• Arrange your custome data dir custome_scene in the same way as the example data:

dfr/
|—— code/...
|—— result/...
|—— data/
|   |—— custome_scene/
|   |   |—— images/
|   |   |   |—— img_0.jpg
|   |   |   |—— ...
|   |   |   |—— img_n.jpg
|   |   |—— cameras.txt
|   |   |—— images.txt
|   |   |—— mesh.obj
|   |—— ...

Citation

@article{DaiDFR_CGF2022,
    author = {Dai, Pinxuan and Xie, Ning},
    title = {Deep Flow Rendering: View Synthesis via Layer-aware Reflection Flow},
    journal = {Computer Graphics Forum},
    volume = {41},
    number = {4},
    pages = {139-148},
    doi = {https://doi.org/10.1111/cgf.14593},
    year = {2022}
}