Tracking Everything Everywhere All at Once
In the field of motion estimation, a remarkable breakthrough has just arrived! Introducing OmniMotion, an innovative method that pioneers a complete and globally consistent motion representation. OmniMotion moves beyond the constraints of traditional optical flow or particle video tracking algorithms that are hindered by limited temporal windows and difficulties in maintaining global consistency of estimated motion trajectories. Instead, OmniMotion enables accurate, full-length motion estimation of every pixel in a video sequence - a truly remarkable feat.
OmniMotion represents a video using a quasi-3D canonical volume and accomplishes pixel-wise tracking via the transformation between local and canonical spaces. This representation doesn't just ensure global consistency; it also opens the doors to tracking through occlusions and modeling any mixture of camera and object motion. The extensive evaluations conducted on the TAP-Vid benchmark and real-world footage have proven that OmniMotion outperforms existing state-of-the-art methods by a substantial margin, both quantitatively and qualitatively.
Paper link: https://arxiv.org/abs/2306.05422
Project link: https://omnimotion.github.io/
A detailed unofficial overview of the paper: https://artgor.medium.com/paper-review-tracking-everything-everywhere-all-at-once-27caa13918bc
#deeplearning #cv #motionestimation
In the field of motion estimation, a remarkable breakthrough has just arrived! Introducing OmniMotion, an innovative method that pioneers a complete and globally consistent motion representation. OmniMotion moves beyond the constraints of traditional optical flow or particle video tracking algorithms that are hindered by limited temporal windows and difficulties in maintaining global consistency of estimated motion trajectories. Instead, OmniMotion enables accurate, full-length motion estimation of every pixel in a video sequence - a truly remarkable feat.
OmniMotion represents a video using a quasi-3D canonical volume and accomplishes pixel-wise tracking via the transformation between local and canonical spaces. This representation doesn't just ensure global consistency; it also opens the doors to tracking through occlusions and modeling any mixture of camera and object motion. The extensive evaluations conducted on the TAP-Vid benchmark and real-world footage have proven that OmniMotion outperforms existing state-of-the-art methods by a substantial margin, both quantitatively and qualitatively.
Paper link: https://arxiv.org/abs/2306.05422
Project link: https://omnimotion.github.io/
A detailed unofficial overview of the paper: https://artgor.medium.com/paper-review-tracking-everything-everywhere-all-at-once-27caa13918bc
#deeplearning #cv #motionestimation
omnimotion.github.io
Tracking Everything Everywhere All at Once