arielreplicate / robust_video_matting

extract foreground of a video

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Examples

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Run time and cost

This model costs approximately $0.046 to run on Replicate, or 21 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 4 minutes. The predict time for this model varies significantly based on the inputs.

Readme

Robust Video Matting (RVM)

English | 中文

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.

Showreel

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

Speed

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 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.


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