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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.
- [Aug 25 2021] Source code and pretrained models are published.
- [Jul 27 2021] Paper is accepted by WACV 2022.
Watch the showreel to see the model's performance.
All footage in the video are available in Google Drive and Baidu Pan (code: tb3w).
Try webcam demo. Visualize the model in your browser.
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.
All models are available in Google Drive and Baidu Pan (code: gym7).
- Install dependencies:
pip install -r requirements_inference.txt- 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'))- 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.
)- Or write your own inference code:
from torch.utils.data 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.Please see inference documentation for details on downsample_ratio hyperparameter, more converter arguments, and more advanced usage.
Please refer to the training documentation to train and evaluate your own model.
Speed is measured with inference_speed_test.py 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 usesdownsample_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.