Robust Video Matting (RVM)

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Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specifically designed for robust human video matting. Unlike existing neural models that process frames as independent images, 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. The project was developed at ByteDance Inc.

News

• [Nov 03 2021] Fixed a bug in train.py.
• [Sep 16 2021] Code is re-released under GPL-3.0 license.
• [Aug 25 2021] Source code and pretrained models are published.
• [Jul 27 2021] Paper is accepted by WACV 2022.

Showreel

Watch the showreel video (YouTube, Bilibili) to see the model's performance.

All footage in the video are available in Google Drive.

Demo

• Webcam Demo: Run the model live in your browser. Visualize recurrent states.
• Colab Demo: Test our model on your own videos with free GPU.

Download

We recommend MobileNetv3 models for most use cases. ResNet50 models are the larger variant with small performance improvements. Our model is available on various inference frameworks. See inference documentation for more instructions.

Framework	Download	Notes
PyTorch	rvm_mobilenetv3.pth rvm_resnet50.pth	Official weights for PyTorch. Doc
TorchHub	Nothing to Download.	Easiest way to use our model in your PyTorch project. Doc
TorchScript	rvm_mobilenetv3_fp32.torchscript rvm_mobilenetv3_fp16.torchscript rvm_resnet50_fp32.torchscript rvm_resnet50_fp16.torchscript	If inference on mobile, consider export int8 quantized models yourself. Doc
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