over extended periods. However, the road to achieving that is not without its challenges. “The first challenge was to formulate the problem because it’s different from what most people did before,” Qianqian explains. “We have sparse feature tracking, which gives you long-range correspondences but they are sparse. On the other hand, we have optical flow, which gives you dense correspondences, but only for a very short period of time. What we want is dense and long-range correspondences. It took a little bit of time to figure that out.” An important moment in the project was realizing the need for invertible mapping. Without it, the global consistency of estimated motion trajectories could not be guaranteed. It was then a challenge to determine how to represent the geometry. Parameterizing the quasi3D space was far from straightforward, 5 DAILY ICCV Friday Qianqian Wang
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