We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting.

Features

  • RVM is specifically designed for robust human video matting
  • RVM uses a recurrent neural network to process videos with temporal memory
  • RVM can perform matting in real-time on any videos without additional inputs
  • It achieves 4K 76FPS and HD 104FPS on an Nvidia GTX 1080 Ti GPU
  • Our model is available in PyTorch, TorchScript, ONNX, TensorFlow, and TensorFlow.js
  • Our model learns to reconstruct the background and keep track of the motion history

Project Samples

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Categories

Video Editing

License

GNU General Public License version 3.0 (GPLv3)

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Additional Project Details

Programming Language

Python

Related Categories

Python Video Editing Software

Registered

2023-03-24