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Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid Dataset and our top-performing TAPIR model.

TAPIR is a two-stage algorithm which employs two stages: 1) a matching stage, which independently locates a suitable candidate point match for the query pointon every other frame, and (2) a refinement stage, which updates both the trajectory and query features based on local correlations. The resulting model is fast and surpasses all prior methods by a significant margin on the TAP-Vid benchmark.

TAP-Vid is a benchmark for models that perform this task, with a collection of ground-truth points for both real and synthetic videos.

This repository contains the following:

TAPIR Demos

The simplest way to run TAPIR is to use our Colab demos online. You can also clone this repo and run TAPIR on your own hardware, including a realtime demo.

Colab Demo

You can run colab demos to see how TAPIR works. You can also upload your own video and try point tracking with TAPIR. We provide two colab demos:

  1. The standard TAPIR colab demo: This is the most powerful TAPIR model that runs on a whole video at once. We mainly report the results of this model in the paper.
  2. The online TAPIR colab demo: This is the sequential TAPIR model that allows for online tracking on points, which can be run in realtime on a GPU platform.

Running TAPIR Locally

Clone the repository:

git clone https://github.com/deepmind/tapnet.git

Switch to the project directory:

cd tapnet

Install requirements for inference:

pip install -r requirements_inference.txt

Download the checkpoint

mkdir checkpoints
wget -P checkpoints https://storage.googleapis.com/dm-tapnet/causal_tapir_checkpoint.npy

Add current path (parent directory of where TapNet is installed) to PYTHONPATH:

export PYTHONPATH=`(cd ../ && pwd)`:`pwd`:$PYTHONPATH

If you want to use CUDA, make sure you install the drivers and a version of JAX that's compatible with your CUDA and CUDNN versions. Refer to the jax manual to install JAX version with CUDA.

You can then run a pretrained causal TAPIR model on a live camera and select points to track:

cd ..
python3 ./tapnet/live_demo.py \

In our tests, we achieved ~17 fps on 480x480 images on a quadro RTX 4000.

TAP-Vid Benchmark

TAP-Vid is a dataset of videos along with point tracks, either manually annotated or obtained from a simulator. The aim is to evaluate tracking of any trackable point on any solid physical surface. Algorithms receive a single query point on some frame, and must produce the rest of the track, i.e., including where that point has moved to (if visible), and whether it is visible, on every other frame. This requires point-level precision (unlike prior work on box and segment tracking) potentially on deformable surfaces (unlike structure from motion) over the long term (unlike optical flow) on potentially any object (i.e. class-agnostic, unlike prior class-specific keypoint tracking on humans). Here are examples of what is annotated on videos of the DAVIS and Kinetics datasets:

tap_vid_zoom_v9.mp4

Our full benchmark incorporates 4 datasets: 30 videos from the DAVIS val set, 1000 videos from the Kinetics val set, 50 synthetic Deepmind Robotics videos for evaluation, and (almost infinite) point track ground truth on the large-scale synthetic Kubric dataset for training.

For more details of downloading and visualization of the dataset, please see data section.

We also include a point tracking model TAP-Net, with code to train it on Kubric dataset. TAP-Net outperforms both optical flow and structure-from-motion methods on the TAP-Vid benchmark while achieving state-of-the-art performance on unsupervised human keypoint tracking on JHMDB, even though the model tracks points on clothes and skin rather than the joints as intended by the benchmark.

Evaluating on TAP-Vid

evaluation_datasets.py is intended to be a stand-alone, copy-and-pasteable reader and evaluator, which depends only on numpy and other basic tools. Tensorflow is required only for reading Kubric (which provides a tensorflow reader by default) as well as file operations, which should be straightforward to replace for systems without Tensorflow.

For each dataset, there is a basic reader which will produce examples, dicts of numpy arrays containing the video, the query points, the target points, and the occlusion flag. Evaluation datasets may be used with one of two possible values for query_mode: strided (each trajectory is queried multiple times, with a fixed-length stride between queries) or first (each trajectory is queried once, with only the first visible point on the query). For details on outputs, see the documentation for sample_queries_strided and sample_queries_first.

To compute metrics, use compute_tapvid_metrics in the same file. This computes results on each batch; the final metrics for the paper can be computed by simple averaging across all videos in the dataset. See the documentation for more details.

Note that the outputs for a single query point should not depend on the other queries defined in the batch: that is, the outputs should be the same whether the queries are passed one at a time or all at once. This is important because the other queries may leak information about how pixels are grouped and how they move. This property is not enforced in the current evaluation code, but algorithms which violate this principle should not be considered valid competitors on this benchmark.

Our readers also supply videos resized at 256x256 resolution. If algorithms can handle it, we encourage using full-resolution videos instead; we anticipate that predictions on such videos would be scaled to match a 256x256 resolution before computing metrics. Such predictions would, however, be evaluated as a separate category: we don't consider them comparable to those produced from lower-resolution videos.

A note on coordinates

In our storage datasets, (x, y) coordinates are typically in normalized raster coordinates: i.e., (0, 0) is the upper-left corner of the upper-left pixel, and (1, 1) is the lower-left corner of the lower-right pixel. Our code, however, immediately converts these to regular raster coordinates, matching the output of the Kubric reader: (0, 0) is the upper-left corner of the upper-left pixel, while (h, w) is the lower-right corner of the lower-right pixel, where h is the image height in pixels, and w is the respctive width.

When working with 2D coordinates, we typically store them in the order (x, y). However, we typically work with 3D coordinates in the order (t, y, x), where y and x are raster coordinates as above, but t is in frame coordinates, i.e. 0 refers to the first frame, and 0.5 refers to halfway between the first and second frames. Please take care with this: one pixel error can make a difference according to our metrics.

Comparison of Tracking With and Without Optical Flow

When annotating videos, we interpolate between the sparse points that the annotators choose by finding tracks which minimize the discrepancy with the optical flow while still connecting the chosen points. To validate that this is indeed improving results, we annotated several DAVIS videos twice and compare them side by side, once using the flow-based interpolation, and again using a naive linear interpolation, which simply moves the point at a constant velocity between points.

TAP-Net and TAPIR training and inference

Install ffmpeg on your machine:

sudo apt update

sudo apt install ffmpeg

Install OpenEXR:

sudo apt-get install libopenexr-dev

Clone the repository:

git clone https://github.com/deepmind/tapnet.git

Add current path (parent directory of where TapNet is installed) to PYTHONPATH:

export PYTHONPATH=`(cd ../ && pwd)`:`pwd`:$PYTHONPATH

Switch to the project directory:

cd tapnet

Install kubric as a subdirectory:

git clone https://github.com/google-research/kubric.git

Install requirements:

pip install -r requirements.txt

If you want to use CUDA, make sure you install the drivers and a version of JAX that's compatible with your CUDA and CUDNN versions. Refer to the jax manual to install JAX version with CUDA.

Usage

The configuration file is located at: ./configs/tapnet_config.py.

You can modify it for your need or create your own config file following the example of tapnet_config.py.

To launch experiment run the command:

python ./experiment.py --config ./configs/tapnet_config.py

or

python ./experiment.py --config ./configs/tapir_config.py

Evaluation

You can run evaluation for a particular dataset (i.e. tapvid_davis) using the command:

python3 ./tapnet/experiment.py \
  --config=./tapnet/configs/tapir_config.py \
  --jaxline_mode=eval_davis_points \
  --config.checkpoint_dir=./tapnet/checkpoint/ \
  --config.experiment_kwargs.config.davis_points_path=/path/to/tapvid_davis.pkl

Available eval datasets are listed in supervised_point_prediction.py.

Download a baseline checkpoint

tapnet/checkpoint/ must contain a file checkpoint.npy that's loadable using our NumpyFileCheckpointer. You can download a checkpoint here, which was obtained via the open-source version of the code, and should closely match the one used to write the paper.

Inference

You can run inference for a particular video (i.e. horsejump-high.mp4) using the command:

python3 ./tapnet/experiment.py \
  --config=./tapnet/configs/tapnet_config.py \
  --jaxline_mode=eval_inference \
  --config.checkpoint_dir=./tapnet/checkpoint/ \
  --config.experiment_kwargs.config.inference.input_video_path=horsejump-high.mp4 \
  --config.experiment_kwargs.config.inference.output_video_path=result.mp4 \
  --config.experiment_kwargs.config.inference.resize_height=256 \
  --config.experiment_kwargs.config.inference.resize_width=256 \
  --config.experiment_kwargs.config.inference.num_points=20

The inference only serves as an example. It will resize the video to 256x256 resolution, sample 20 random query points on the first frame and track these random points in the rest frames.

Also note that the current checkpoint is trained under 256x256 resolution and has not been trained for other resolutions.

Citing this work

Please use the following bibtex entry to cite our work:

@inproceedings{doersch2022tapvid,
  author = {Doersch, Carl and Gupta, Ankush and Markeeva, Larisa and
            Continente, Adria Recasens and Smaira, Kucas and Aytar, Yusuf and
            Carreira, Joao and Zisserman, Andrew and Yang, Yi},
  title = {TAP-Vid: A Benchmark for Tracking Any Point in a Video},
  booktitle={NeurIPS Datasets Track},
  year = {2022},
}

License and disclaimer

Copyright 2022 DeepMind Technologies Limited

All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at:

https://www.apache.org/licenses/LICENSE-2.0

All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode In particular the annotations of TAP-Vid, as well as the RGB-Stacking videos, are released under a Creative Commons BY license. The original source videos of DAVIS come from the val set, and are also licensed under creative commons licenses per their creators; see the DAVIS dataset for details. Kinetics videos are publicly available on YouTube, but subject to their own individual licenses. See the Kinetics dataset webpage for details.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.

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