aharley
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pips

Nima Boscarino commited on Sep 3, 2022

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@@ -18,7 +18,7 @@ From the paper abstract:
 > [...] we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions.
-![](https://camo.githubusercontent.com/6313516710b2b7dcb03deebcd423f39f77aa03f0ad378e30a876e807e5391cee/68747470733a2f2f7061727469636c652d766964656f2d7265766973697465642e6769746875622e696f2f696d616765732f666967312e6a7067)
 # Citation

 > [...] we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions.
+![](https://particle-video-revisited.github.io/images/fig1.jpg)
 # Citation