  We introduce PointOdyssey, a large-scale synthetic dataset, and data
generation framework, for the training and evaluation of long-term fine-grained
tracking algorithms. Our goal is to advance the state-of-the-art by placing
emphasis on long videos with naturalistic motion. Toward the goal of
naturalism, we animate deformable characters using real-world motion capture
data, we build 3D scenes to match the motion capture environments, and we
render camera viewpoints using trajectories mined via structure-from-motion on
real videos. We create combinatorial diversity by randomizing character
appearance, motion profiles, materials, lighting, 3D assets, and atmospheric
effects. Our dataset currently includes 104 videos, averaging 2,000 frames
long, with orders of magnitude more correspondence annotations than prior work.
We show that existing methods can be trained from scratch in our dataset and
outperform the published variants. Finally, we introduce modifications to the
PIPs point tracking method, greatly widening its temporal receptive field,
which improves its performance on PointOdyssey as well as on two real-world
benchmarks. Our data and code are publicly available at:
https://pointodyssey.com
