Causal Spatiotemporal State Evolution for Monocular 4D Reconstruction
While feed-forward models excel in static 3D geometry estimation, extending them to dynamic 4D scenes requires modeling visual geometry as a causally evolving state. Current methods typically rely on unordered temporal aggregation, which lacks the directional structure of physical time. Furthermore, existing supervision often treats geometry and motion as disjoint objectives, failing to exploit their inherent physical coupling. Consequently, they lack a unified constraint to effectively supervise temporal state transitions, leading to inconsistent 4D scene representations. To address this, we propose a feed-forward framework for causal 4D visual geometry learning from monocular videos. At...
| Authors | Qiaowei Miao, Yijing Guo, Lipei Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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