FTP-VM (CVPR2023)

Implementation and videos of End-to-End Video Matting With Trimap Propagation in CVPR2023.

[CVPR OpenAccess] [Presentation Video] [Paper PDF] [Supplementary Video]

FTP-VM tries to integrate the trimap propagation and video matting into 1 model, and improves the efficiency. It can matte a 1024x576 video in 40 FPS on a RTX2080ti GPU, while previous works are in about 5 FPS.

Given 1 or few pairs of memory trimaps and frames, FTP-VM is able to matte a video with arbitrary salient objects.
We hope this work can encourage future research of fast universal video matting

Roadmap

[x] Clean the training code & data
[x] Clean the dataset inference code & data
[ ] Workaround for inference on TCVOM/OTVM
[ ] Upload more supplementary videos
[ ] (Possibly) Collaborate with SegmentAnyThing

Installation

The version of pytorch used in our experiments is 1.8.2, but it should work with other versions.\ Install pytorch in the old version at https://pytorch.org/get-started/previous-versions \ Feel free to edit versions of packages for convenience.

pip install -r requirements.txt

Model Usage

For those who want to use the model via code directly.

import torch
from FTPVM.model import FastTrimapPropagationVideoMatting as FTPVM
model = FTPVM()
model.load_state_dict(torch.load('saves/ftpvm.pth'))

Usage

# Images are in [0, 1] with size of (batch, time, channel, height, width)
# Memory has 1 frame per batch, and trimap (mask) has 1 channel.
query_imgs = torch.rand((2, 4, 3, 256, 256))
memory_imgs = torch.rand((2, 1, 3, 256, 256))
memory_trimaps = torch.rand((2, 1, 1, 256, 256))
# General forward
trimaps, boundary_mattes, full_mattes, recurrent_mems = model(query_imgs, memory_imgs, memory_trimaps)
# Forward with RNN memory
trimaps, boundary_mattes, full_mattes, recurrent_mems = model(query_imgs, memory_imgs, memory_trimaps, *recurrent_mems)
# Preserve memory key & values in Memory matching, which is useful in application
memory_key_val = model.encode_imgs_to_value(memory_imgs, memory_trimaps)
trimaps, boundary_mattes, full_mattes, recurrent_mems = model.forward_with_memory(query_imgs, *memory_key_val, *recurrent_mems)

Inference

inference_model_list.py define the (arbitrary name, model name in model/which_model.py, Inference class, model path)\ Inference programs will use the defined name to find and load the model.

Dataset

Prepare

Please place the desired datasets into the same folder (or by symbolic link).

VM108: The same source as training data https://github.com/yunkezhang/TCVOM#videomatting108-dataset
RVM
- Download VM240k HD format at https://grail.cs.washington.edu/projects/background-matting-v2/#/datasets
- Download DVM background video https://drive.google.com/file/d/1n2GMVnqJgihypwH_9IiHbhP9PWeCgpEt/view?usp=sharing and unzip it
- Run
```
python generate_videomatte_with_background_video.py \
--videomatte-dir ../dataset/VideoMatte240K_JPEG_HD/test \
--background-dir ../dataset/dvm_bg \
--out-dir ../dataset/videomatte_motion_1024 \
--resize 1024 576 \
--trimap_width 25
```
- Edit parameters if you want to inference with different resolution
Real Human Dataset: https://github.com/TiantianWang/VideoMatting-CRGNN

Inference

The following files will be generated by default

OUT_ROOT
- DATASET_NAME
- EXPERIMENT_NAME
  - DATASET_SUBNAME
  - clip1
    - pha
    - 0000.png
    - 0001.png
  - clip2
    - ...
  - clip1.mp4
  - clip2.mp4
  - MODLE_NAME.xlsx
- EXPERIMENT_NAME2
  - ...
- GT

For generel inference on datasets

usage: inference_dataset.py [-h] [--size SIZE] [--batch_size BATCH_SIZE] [--n_workers N_WORKERS]
                            [--gpu GPU] [--trimap_width TRIMAP_WIDTH] [--disable_video]
                            [--downsample_ratio DOWNSAMPLE_RATIO] [--out_root OUT_ROOT]
                            [--dataset_root DATASET_ROOT] [--disable_vm108] [--disable_realhuman]
                            [--disable_vm240k]

optional arguments:
  -h, --help            show this help message and exit
  --size SIZE           eval video size: sd, 1024, hd, 4k
  --batch_size BATCH_SIZE
                        frames in a batch
  --n_workers N_WORKERS
                        num workers
  --gpu GPU
  --trimap_width TRIMAP_WIDTH default=25
  --disable_video       Without savinig videos
  --downsample_ratio DOWNSAMPLE_RATIO default=1
  --out_root OUT_ROOT
  --dataset_root DATASET_ROOT
  --disable_vm108       Without VM108
  --disable_realhuman   Without RealHuman
  --disable_vm240k      Without VM240k

python inference_dataset.py --dataset_root ../dataset --out_root inference

For inference on VM108 with different memory update period

python inference_dataset_update_mem.py --dataset_root ../dataset --out_root inference  --memory_freq 30 60 120 240 480 1

memory_freq: Update memory in N frames. 1 for each frame, i.e. matting only.

Webcam (Manual to be updated)

still not robust enough to webcam frames :(

python webcam.py

Raw video

The code is borrowed from RVM

usage: python inference_footages.py [-h] --root ROOT --out_root OUT_ROOT
                             [--gpu GPU] [--target_size TARGET_SIZE]
                             [--seq_chunk SEQ_CHUNK]
optional arguments:
  -h, --help            show this help message and exit
  --root ROOT           input video root
  --out_root OUT_ROOT   output video root
  --gpu GPU             gpu id, default = 0
  --target_size TARGET_SIZE
                        downsample the video by ratio of the larger width
                        to target_size, and upsampled back by FGF.
                        default = 1024
  --seq_chunk SEQ_CHUNK
                        frames to process in a batch
                        default = 4

You need to put 1 video with 1 thumbnail & trimap as memory pairs at least, where the thumbnail is suggested but not required to be the first frame. More trimaps will generate different results.

- root
  - video1.mp4
  - video1_thumbnail.png
  - video1_trimap.png
  - video1_trimap2.png
  - ...
- out_root
  - video1__com.mp4
  - video1__fgr.mp4
  - video1__pha.mp4
  - video1_2_com.mp4
  - video1_2_fgr.mp4
  - video1_2_pha.mp4
  - ...

For more precised control, please refer to inference_footages_util.py.

Workaround for inference on related works (TBD)

TCVOM

OTVM

- Precomposite dataset by running `python .py` - Clone the repo - Copy `.py` into OTVM root folder, and run `python .py` - Evaluate by running`python evaluation/evaluate_lr.py`

Training

Dataset

Please put them in dataset folder at the same level (or symbolic link) as FTP-VM folder (this repo).

- dataset
  - Distinctions646
    - Train
  - VideoMatting108
    - BG_done
        - (Video 0)
        - (Video 1)
        - ...
    - FG_done
        - ...
    - train_videos.txt
    - val_videos.txt
    - frame_corr.json
  - BG20k
    - BG-20k
  - YoutubeVIS
    - train

- FTP-VM (Model folder)
  - train.py
  - ...

Image Matting Dataset: D646
Video Matting Dataset: VM108
Video Object Segmentation Dataset: YoutubeVIS 2019
Background Image Dataset: BG-20k

If you just want to train on VM108 dataset, please read VM108 dataset only.

Run

Main setting

python train.py \
--id FTPVM \
--which_model FTPVM \
--num_worker 12 \
--benchmark \
--lr 0.0001 -i 120000 \
--iter_switch_dataset 30000 \
--use_background_dataset \
-b_seg 8 -b_img_mat 10 -b_vid_mat 4 \
-s_seg 8 -s_img_mat 4 -s_vid_mat 8 \
--seg_cd 20000 --seg_iter 10000 --seg_start 0 --seg_stop 100000 \
--size 480 \
--tvloss_type temp_seg_allclass_weight

VM108 dataset only

python train.py \
--id FTPVM_VM108_only \
--which_model FTPVM \
--num_worker 12 \
--benchmark \
--lr 0.0001 -i 120000 \
--iter_switch_dataset 0 \
-b_vid_mat 4 -s_vid_mat 8 --seg_stop -1 \
--size 480 \
--tvloss_type temp_seg_allclass_weight

Simple explanation

- `--id` : experiment name - `--which_model` : defined model name in `model/which_model.py` - `--use_background_dataset` : composite the data with an additional BG20k dataset as well - `--iter_switch_dataset 30000` : switch to video dataset at N iter - `-b_seg 8 -b_img_mat 10 -b_vid_mat 4` : batch size of datasets - `-s_seg 8 -s_img_mat 4 -s_vid_mat 8` : sequence / clip length of datasets - `--seg_cd 20000 --seg_iter 10000 --seg_start 0 --seg_stop 100000` : \ segmentation training starts at 0th iter, runs for 10000 iters followed by 20000-iters cooldown, stop at 100000th iter. - `--tvloss_type` : variant of segmentation inconsistency loss

Citation

@InProceedings{Huang_2023_CVPR,
    author    = {Huang, Wei-Lun and Lee, Ming-Sui},
    title     = {End-to-End Video Matting With Trimap Propagation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {14337-14347}
}

Useful Implementation

RVM
- Fast Guided Filter
- Inference code
MiVOS
- STCN
- XMem
- MiVOS inspired me to leverage video object segmentation on video matting (though it's been already applied in earlier researches)
- Overall architecture
- Their works are so amazing :)

License

While the code is under GNU General Public License v3.0, the usage of pre-trained weight might be limited due to the training data.

csvt32745 / FTP-VM

readme

FTP-VM (CVPR2023)

Roadmap

Installation

Model Usage

Inference

Dataset

Prepare

Inference

Webcam (Manual to be updated)

Raw video

Workaround for inference on related works (TBD)

Training

Dataset

Run

Main setting

VM108 dataset only

Citation

Useful Implementation

License