Causal Spatiotemporal State Evolution for Monocular 4D Reconstruction

Abstract

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 its core, Causal Spatiotemporal Mamba augments temporal aggregation with directional state propagation. Through complementary Temporal-Spatial and Spatial-Temporal Scans, CSM continuously propagates geometry-aware states across frames to model spatiotemporal evolution. To align this causal state evolution with observable dynamics, we introduce Unified Motion Supervision. Rather than treating motion as an independent objective, UMS leverages geometry-aware predictions from the camera and depth heads to construct a unified forward motion field that is consistent with the underlying 3D structure. This geometry-grounded supervision explicitly constrains inter-frame state transitions, enabling stable states propagation across frames. Extensive evaluations on dynamic benchmarks demonstrate state-of-the-art performance across video depth, camera pose, and optical flow estimation. These results validate that the synergy between the directional state propagation of Causal Spatiotemporal Mamba and Unified Motion Supervision effectively captures continuous scene evolution, significantly reducing temporal drift and reinforcing spatial-temporal consistency in 4D representation learning.