Point4D: Long-range 4D Motion Reconstruction

2026-09-08

AI Summary

Point4D通过引入3D查询式运动解码器解决长视频序列的4D重建问题，实现多百帧视频中的点轨迹预测。

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 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

Problem

• 4D Reconstruction
• Long-range Video
• 3D Trajectories

Innovation

• 4D Reconstruction
• Long-range Video Sequences
• 3D Query-based Motion Decoder
• Visual Descriptor Reuse