DriveWeave: Feed-Forward Dynamic Scene Geometry for Autonomous Driving

Reconstructing dynamic driving scenes requires jointly modeling 3D geometry and its temporal evolution. Existing paradigms typically estimate geometry and motion independently, lacking explicit mechanisms to couple their evolution and resulting in spatiotemporal scale drift and structural misalignment. We present DriveWeave, a unified feed-forward foundation paradigm for autonomous driving that predicts metric depth, optical flow, 6-DoF ego-motion, and 3D Gaussian primitives from surround-view videos in a single pass. To resolve the quadratic complexity of dense 4D attention, we introduce 4D Synchronized Register Attention (4D-SyncAttn), which leverages view-frame register synchronization to enable efficient global context aggregation. Furthermore, we formulate a flow-driven spatiotemporal evolution mechanism that enforces complementary differentiable photometric and geometric constraints across frames, encouraging a unified and temporally consistent representation of geometry, motion, and 3DGS rendering manifolds. Extensive evaluations across diverse driving benchmarks demonstrate that DriveWeave achieves state-of-the-art performance in metric depth estimation, 3D point cloud reconstruction, ego-motion estimation, and gaussian scene rendering with superior feed-forward efficiency.