Some details are missing in the paper

Dear authors, thank you for your great work and congratulations for the publication at CVPR 2025 ! After reading your paper, I feel like some details are still missing.

First, about your data curation pipeline, you explain that the **optical flow score** is used to filter videos (mostly static ones I guess) without giving details on how this score is computed. The given reference [1] don't explicitly mention it either. Can you give more details on that and also explain the threshold of 3.

Secondly, you explain that you use camera motion detection and **motion segmentation** [2] for filtering out videos with significant camera motion. Can you give more details on how do you do this ? For instance how you get the center points of each object, and also elaborate a little bit more on the given thresholds ?

Finally, In order to evaluate the trajectory controllability, you explain that you use the average L1 distance between predefined and generated trajectories. How do you extract the trajectories of the generated videos ? Formally, how to express the **TrajError** metric ?

Having more details on these points would be much appreciated, and again congratulations for your great work ! I might be wrong on some remarks, my apologies if that's the case.

### References
[1] Xu et. al, Unifying flow, stereo and depth estimation, IEEE TPAMI 2023
[2] Zhao et. al, ParticleSfM: Exploiting dense point trajectories for localizing moving cameras in the wild, ECCV 2022

Start by reviewing the paper's data curation and trajectory evaluation descriptions, along with references [1] and [2]. Document how the optical-flow score, camera-motion and motion-segmentation thresholds, object centers, and generated trajectories are obtained, and formally define TrajError. Done means all three questions are answered with enough detail to reproduce the described evaluation.

Written by the indexing model from the issue text.