FBA Matting

Official repository for the paper F, B, Alpha Matting. This paper and project is under heavy revision for peer reviewed publication, and so I will not be able to release the training code yet.
Marco Forte¹, François Pitié¹

¹ Trinity College Dublin

Requirements

GPU memory >= 11GB for inference on Adobe Composition-1K testing set, more generally for resolutions above 1920x1080.

Packages:

• torch >= 1.4
• numpy
• opencv-python

Additional Packages for jupyter notebook

• matplotlib
• gdown (to download model inside notebook)

Models

These models have been trained on Adobe Image Matting Dataset. They are covered by the Adobe Deep Image Mattng Dataset License Agreement so they can only be used and distributed for noncommercial purposes.
More results of this model avialiable on the alphamatting.com, the videomatting.com benchmark, and the supplementary materials PDF.

Model Name	File Size	SAD	MSE	Grad	Conn
FBA Table. 4	139mb	26.4	5.4	10.6	21.5

Prediction

We provide a script demo.py and jupyter notebook which both give the foreground, background and alpha predictions of our model. The test time augmentation code will be made availiable soon.
In the torchscript notebook we show how to convert the model to torchscript.

In this video I demonstrate how to create a trimap in Pinta/Paint.NET.

Training

Training code is not released at this time. It may be released upon acceptance of the paper. Here are the key takeaways from our work with regards training.

• Use a batch-size of 1, and use Group Normalisation and Weight Standardisation in your network.
• Train with clipping of the alpha instead of sigmoid.
• The L1 alpha, compositional loss and laplacian loss are beneficial. Gradient loss is not needed.
• For foreground prediction, we extend the foreground to the entire image and define the loss on the entire image or at least the unknown region. We found this better than solely where alpha>0. Code for foreground extension

Citation

@article{forte2020fbamatting,
  title   = {F, B, Alpha Matting},
  author  = {Marco Forte and François Pitié},
  journal = {CoRR},
  volume  = {abs/2003.07711},
  year    = {2020},
}

Related works of ours

• 99% accurate interactive object selection with just a few clicks: PDF, Code