Robust Video Matting (RVM)


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.


  • [Aug 25 2021] Source code and pretrained models are published.
  • [Jul 27 2021] Paper is accepted by WACV 2022.


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

All footage in the video are available in Google Drive and Baidu Pan (code: tb3w).


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


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
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
If inference on mobile, consider export int8 quantized models yourself. Doc
ONNX rvm_mobilenetv3_fp32.onnx
Tested on ONNX Runtime with CPU and CUDA backends. Provided models use opset 12. Doc, Exporter.
TensorFlow 2 SavedModel. Doc
Run the model on the web. Demo, Starter Code
CoreML rvm_mobilenetv3_1280x720_s0.375_fp16.mlmodel
CoreML does not support dynamic resolution. Other resolutions can be exported yourself. Models require iOS 13+. s denotes downsample_ratio. Doc, Exporter

All models are available in Google Drive and Baidu Pan (code: gym7).

PyTorch Example

  1. Install dependencies:
pip install -r requirements_inference.txt
  1. Load the model:
import torch from model import MattingNetwork model = MattingNetwork('mobilenetv3').eval().cuda() # or "resnet50" model.load_state_dict(torch.load('rvm_mobilenetv3.pth'))
  1. To convert videos, we provide a simple conversion API:
from inference import convert_video convert_video( model, # The model, can be on any device (cpu or cuda). input_source='input.mp4', # A video file or an image sequence directory. output_type='video', # Choose "video" or "png_sequence" output_composition='output.mp4', # File path if video; directory path if png sequence. output_video_mbps=4, # Output video mbps. Not needed for png sequence. downsample_ratio=None, # A hyperparameter to adjust or use None for auto. seq_chunk=12, # Process n frames at once for better parallelism. )
  1. Or write your own inference code:
from import DataLoader from torchvision.transforms import ToTensor from inference_utils import VideoReader, VideoWriter reader = VideoReader('input.mp4', transform=ToTensor()) writer = VideoWriter('output.mp4', frame_rate=30) bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # Green background. rec = [None] * 4 # Initial recurrent states. downsample_ratio = 0.25 # Adjust based on your video. with torch.no_grad(): for src in DataLoader(reader): # RGB tensor normalized to 0 ~ 1. fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio) # Cycle the recurrent states. com = fgr * pha + bgr * (1 - pha) # Composite to green background. writer.write(com) # Write frame.
  1. The models and converter API are also available through TorchHub.
# Load the model.
model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50"

# Converter API.
convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter")

Please see inference documentation for details on downsample_ratio hyperparameter, more converter arguments, and more advanced usage.

Training and Evaluation

Please refer to the training documentation to train and evaluate your own model.


Speed is measured with for reference.

GPU dType HD (1920x1080) 4K (3840x2160)
RTX 3090 FP16 172 FPS 154 FPS
RTX 2060 Super FP16 134 FPS 108 FPS
GTX 1080 Ti FP32 104 FPS 74 FPS
  • Note 1: HD uses downsample_ratio=0.25, 4K uses downsample_ratio=0.125. All tests use batch size 1 and frame chunk 1.
  • Note 2: GPUs before Turing architecture does not support FP16 inference, so GTX 1080 Ti uses FP32.
  • Note 3: We only measure tensor throughput. The provided video conversion script in this repo is expected to be much slower, because it does not utilize hardware video encoding/decoding and does not have the tensor transfer done on parallel threads. If you are interested in implementing hardware video encoding/decoding in Python, please refer to PyNvCodec.

Project Members

## GitHub