Paper Detail
Minsik Jeon, Jay Karhade, Deva Ramanan, Shubham Tulsiani
We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D trajectories across multi-hundred-frame videos, unlike existing 4D methods that are limited to short input windows of at most a few dozen frames. A key innovation that enables this is our flexible 3D query-based motion decoder that decouples trajectory prediction from image-plane visibility. The predicted 3D endpoints are then directly re-queried in the next chunk without re-projection or matching. Furthermore, we show that extracting and reusing a visual descriptor from an arbitrary frame where the point is visible leads to better performance than relying solely on the source patch. Overall, Point4D achieves state-of-the-art performance across diverse long-video tracking benchmarks spanning over 200 frames and largely outperforms previous feed-forward 4D method. Project page: https://point-4d.github.io
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@article{jeon2026point4d,
title = {Point4D: Long-range 4D Motion Reconstruction},
author = {Minsik Jeon and Jay Karhade and Deva Ramanan and Shubham Tulsiani},
year = {2026},
abstract = {We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D trajectories across multi-hundred-frame videos, unlike existing 4D methods that are limited to short input windows of at most a few dozen frames. A key innovation that enables this is our flexible 3D query-based motion decoder that decouples trajectory prediction from image-plane visibility. The predicted 3D endpoints are then directly re-queried in},
url = {https://arxiv.org/abs/2609.09145},
keywords = {cs.CV},
eprint = {2609.09145},
archiveprefix = {arXiv},
}
{}