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Dense Optical Tracking: Connecting the Dots

Guillaume Le Moing, Jean Ponce, Cordelia Schmid <br>

Project Page | Paper | Video | BibTeX

DOT unifies point tracking and optical flow techniques:

• It tracks all pixels in a frame simultaneously.
• It retains the robustness to occlusions and the accuracy of point tracking techniques.
• It enjoys the spatial consistency and runs at a comparable speed to optical flow techniques.

News 📣

• [March 1st, 2024] We now support TAPIR and BootsTAPIR: new SOTA on DAVIS!
• [February 26th, 2024] DOT has been accepted to CVPR 2024!
• [January 1st, 2024] We now support CoTracker2: DOT is up to 2x faster!

Installation

Set Up Environment

Clone the repository.

git clone https://github.com/16lemoing/dot
cd dot

[Optional] Create a conda environment.

conda create -n dot python=3.9
conda activate dot

Install the PyTorch and TorchVision versions which are compatible with your CUDA configuration.

pip install torch==2.0.1 torchvision==0.15.2

Install inference dependencies.

pip install tqdm matplotlib einops einshape scipy timm lmdb av mediapy

[Optional] Install training dependencies.

pip install tensorboard

[Optional] Set up custom modules from PyTorch3D to increase speed and reduce memory consumption of interpolation operations.

cd dot/utils/torch3d/ && python setup.py install && cd ../../..

Model Zoo

Optical flow estimation

→ find motion boundaries

Point tracking initialization

→ track sparse queries, half at motion boundaries, half randomly

Optical flow refinement

→ get dense motion from sparse point tracks

wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/cvo_raft_patch_8.pth
wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/movi_f_raft_patch_4_alpha.pth
wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/movi_f_cotracker_patch_4_wind_8.pth
wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/movi_f_cotracker2_patch_4_wind_8.pth
wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/panning_movi_e_tapir.pth
wget -P checkpoints https://huggingface.co/16lemoing/dot/resolve/main/panning_movi_e_plus_bootstapir.pth

Demo

Download demo data.

wget -P datasets https://huggingface.co/16lemoing/dot/resolve/main/demo.zip
unzip datasets/demo.zip -d datasets/

Spaghetti :spaghetti:

Produce videos from the teaser figure.

python demo.py --visualization_modes spaghetti_last_static --video_path orange.mp4
python demo.py --visualization_modes spaghetti_last_static --video_path treadmill.mp4

https://github.com/16lemoing/dot/assets/32103788/82b888b8-3bc4-4ce8-9278-1adf39eb842a

python demo.py --visualization_modes spaghetti_last_static --video_path umbrella.mp4

https://github.com/16lemoing/dot/assets/32103788/088cc2d6-0cd3-449c-a42a-58f4371427d0

python demo.py --visualization_modes spaghetti_first_last_mask --video_path skateboard.mp4 --mask_path skateboard.png

Overlay :rainbow:

https://github.com/16lemoing/dot/assets/32103788/7cc812c1-67fe-4710-9385-6675dd95cbf9

python demo.py --visualization_modeovis overlay --video_path cartwheel.mp4

https://github.com/16lemoing/dot/assets/32103788/c0581f9a-0508-423b-b4db-9ec1aca2320a

python demo.py --visualization_modes overlay --video_path cartwheel.mp4 --inference_mode tracks_from_every_cell_in_every_frame --cell_time_steps 20

https://github.com/16lemoing/dot/assets/32103788/580ef7eb-4ae0-4174-9d62-9cfb62651c99

python demo.py --visualization_modes overlay_stripes_mask --video_path varanus.mp4 --mask_path varanus.png

Evaluation

Data Preprocessing

wget -P datasets/kubric/cvo https://huggingface.co/datasets/16lemoing/cvo/resolve/main/cvo_test.lmdb
wget -P datasets/kubric/cvo https://huggingface.co/datasets/16lemoing/cvo/resolve/main/cvo_test_extended.lmdb

wget -P datasets/tap https://storage.googleapis.com/dm-tapnet/tapvid_davis.zip
wget -P datasets/tap https://storage.googleapis.com/dm-tapnet/tapvid_kinetics.zip
wget -P datasets/tap https://storage.googleapis.com/dm-tapnet/tapvid_rgb_stacking.zip
unzip "datasets/tap/*.zip" -d datasets/tap/

Compute Evaluation Metrics

python test_cvo.py --split {clean|final|extended}
python test_tap.py --split {davis|kinetics|rgb_stacking}

Benchmarking

Kubric-CVO

We compute the dense motion between the first and last frames of videos, and report:

• the end point error (EPE) of flows
• the intersection over union (IoU) of occluded regions
• the average inference time per video (in seconds) on a NVIDIA V100 GPU

python test_cvo.py --split {final|extended} --model pt --tracker_config configs/tapir.json --tracker_path checkpoints/panning_movi_e_tapir.pth
python test_cvo.py --split {final|extended} --model pt --tracker_config configs/bootstapir.json --tracker_path checkpoints/panning_movi_e_plus_bootstapir.pth
python test_cvo.py --split {final|extended} --model pt --tracker_config configs/cotracker_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker_patch_4_wind_8.pth
python test_cvo.py --split {final|extended} --model pt --tracker_config configs/cotracker2_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker2_patch_4_wind_8.pth
python test_cvo.py --split {final|extended} --model dot --tracker_config configs/cotracker_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker_patch_4_wind_8.pth
python test_cvo.py --split {final|extended} --model dot --tracker_config configs/cotracker2_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker2_patch_4_wind_8.pth

TAP

We compute the dense motion between the query frames (query first mode) and every other frame of videos, and report for ground truth trajectories:

• the average jaccard (AJ)
• the average proportion of points within a threshold (<δ)
• the occlusion accuracy (OA)
• the average inference time per video (in seconds) on a NVIDIA V100 GPU

python test_tap.py --split {davis|rgb_stacking} --query_mode {first|strided} --model dot --tracker_config configs/cotracker_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker_patch_4_wind_8.pth
python test_tap.py --split {davis|rgb_stacking} --query_mode {first|strided} --model dot --tracker_config configs/cotracker2_patch_4_wind_8.json --tracker_path checkpoints/movi_f_cotracker2_patch_4_wind_8.pth
python test_tap.py --split {davis|rgb_stacking} --query_mode {first|strided} --model dot --tracker_config configs/tapir.json --tracker_path checkpoints/panning_movi_e_tapir.pth
python test_tap.py --split {davis|rgb_stacking} --query_mode {first|strided} --model dot --tracker_config configs/bootstapir.json --tracker_path checkpoints/panning_movi_e_plus_bootstapir.pth

Training

Data Preprocessing

Download Kubric-MoviF train data.

wget -P datasets/kubric/movi_f https://huggingface.co/datasets/16lemoing/movi_f/resolve/main/video_part.zip
wget -P datasets/kubric/movi_f https://huggingface.co/datasets/16lemoing/movi_f/resolve/main/video_part.z01
wget -P datasets/kubric/movi_f https://huggingface.co/datasets/16lemoing/movi_f/resolve/main/ground_truth.zip
wget -P datasets/kubric/movi_f https://huggingface.co/datasets/16lemoing/movi_f/resolve/main/cotracker.zip

Unzip data.

zip -F datasets/kubric/movi_f/video_part.zip --out datasets/kubric/movi_f/video.zip
unzip datasets/kubric/movi_f/video.zip -d datasets/kubric/movi_f/
unzip datasets/kubric/movi_f/ground_truth.zip -d datasets/kubric/movi_f/
unzip datasets/kubric/movi_f/cotracker.zip -d datasets/kubric/movi_f/

Install additional dependencies.

pip install joblib tensorflow tensorflow_datasets tensorflow-graphics

Download Kubric-MoviF train data.

python preprocess.py --extract_movi_f

[Requires a GPU] Save tracks from CoTracker for Kubric-MoviF train data.

python preprocess.py --save_tracks

Optimization

python train.py

License

Most of our code is licensed under the MIT License. However, some parts of the code are adapted from external sources and conserve their original license: CoTracker is licensed under CC-BY-NC, RAFT uses the BSD 3-Clause License, Kubric and TAP use the Apache 2.0 License, and PyTorch3D is licensed under a BSD License.

Contributing

We actively encourage contributions. Want to feature a cool application which builds upon DOT, or add support to another point tracker / optical flow model? Don't hesitate to open an issue to discuss about it.

Acknowledgments

We want to thank RAFT, AccFlow, TAP, CoTracker, DinoTracker, OmniMotion and Kubric for publicly releasing their code, models and data.

Citation

Please note that any use of the code in a publication must explicitly refer to:

@inproceedings{lemoing2024dense,
  title = {Dense Optical Tracking: Connecting the Dots},
  author = {Le Moing, Guillaume and Ponce, Jean and Schmid, Cordelia},
  year = {2024},
  booktitle = {CVPR}
}