Large-scale annotated datasets have served as a highway for creating precise models in various computer vision tasks. They wish to offer such a highway on this study to perform fine-grained long-range tracking. Effective-grained long-range tracking goals to follow the matching world surface point for so long as feasible, given any pixel location in any frame of a movie. There are several generations of datasets aimed toward fine-grained short-range tracking (e.g., optical flow) and commonly updated datasets aimed toward various forms of coarse-grained long-range tracking (e.g., single-object tracking, multi-object tracking, video object segmentation). Nonetheless, there are only so many works on the interface between these two forms of monitoring. 

Researchers have already tested fine-grained trackers on real-world movies with sparse human-provided annotations (BADJA and TAPVid) and trained them on unrealistic synthetic data (FlyingThings++ and Kubric-MOVi-E), which consists of random objects moving in unexpected directions on random backdrops. While it’s intriguing that these models can generalize to actual videos, using such basic training prevents the event of long-range temporal context and scene-level semantic awareness. They contend that long-range point tracking shouldn’t be considered an extension of optical flow, where naturalism could also be abandoned without suffering negative consequences. 

While the video’s pixels may move somewhat randomly, their path reflects several modellable elements, resembling camera shaking, object-level movements and deformations, and multi-object connections, including social and physical interactions. Progress relies on people realizing the difficulty’s magnitude, each when it comes to their data and methodology. Researchers from Stanford University suggest PointOdyssey, a big synthetic dataset for long-term fine-grained tracking training and assessment. The intricacy, diversity, and realism of real-world video are all represented of their collection, with pixel-perfect annotation only being attainable through simulation. 

They use motions, scene layouts, and camera trajectories which might be mined from real-world videos and motion captures (versus being random or hand-designed), distinguishing their work from prior synthetic datasets. Additionally they use domain randomization on various scene attributes, resembling environment maps, lighting, human and animal bodies, camera trajectories, and materials. They may also give more photo realism than was previously achievable due to advancements within the accessibility of high-quality content and rendering technologies. The motion profiles of their data are derived from sizable human and animal motion capture datasets. They employ these captures to generate realistic long-range trajectories for humanoids and other animals in outdoor situations. 

In outdoor situations, they pair these actors with 3D objects dispersed randomly on the bottom plane. These items reply to the actors following physics, resembling being kicked away when the feet come into contact with them. Then, they employ motion captures of inside settings to create realistic indoor scenarios and manually recreate the capture environments of their simulator. This permits us to recreate the precise motions and interactions while maintaining the scene-aware character of the unique data. To offer complex multi-view data of the situations, they import camera trajectories derived from real footage and connect extra cameras to the synthetic beings’ heads. In contrast to Kubric and FlyingThings’ largely random motion patterns, they take a capture-driven approach. 

Their data will stimulate the event of tracking techniques that move beyond the traditional reliance solely on bottom-up cues like feature-matching and utilize scene-level cues to supply strong priors on the right track. An unlimited collection of simulated assets, including 42 humanoid forms with artist-created textures, 7 animals, 1K+ object/background textures, 1K+ objects, 20 original 3D sceneries, and 50 environment maps, gives their data its aesthetic diversity. To create quite a lot of dark and vibrant sceneries, they randomize the scene’s lighting. Moreover, they add dynamic fog and smoke effects to their sceneries, adding a style of partial occlusion that FlyingThings and Kubric completely lack. One in every of the brand new problems that PointOdyssey opens is find out how to employ long-range temporal context. 

As an example, the state-of-the-art tracking algorithm Persistent Independent Particles (PIPs) has an 8-frame temporal window. They suggest a couple of changes to PIPs as a primary step towards using arbitrarily lengthy temporal context, including considerably expanding its 8-frame temporal scope and adding a template-update mechanism. In line with experimental findings, their solution outperforms all others regarding tracking accuracy, each on the PointOdyssey test set and on real-world benchmarks. In conclusion, PointOdyssey, a large synthetic dataset for long-term point tracking that tries to reflect the difficulties—and opportunities—of real-world fine-grained monitoring, is the most important contribution of this study.

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