TAG4D-SLAM: Track-Anchored 4D Gaussian SLAM with Feed-Forward Priors

Dynamic scenes remain a fundamental challenge for visual SLAM: moving objects violate the static-world assumption, while simply filtering them out discards valuable scene content. To address this, we present TAG4D-SLAM, a monocular dynamic Gaussian SLAM framework that jointly performs accurate camera tracking and high-fidelity 4D mapping with priors from the feed-forward reconstruction model. Specifically, we augment the feed-forward model with dynamic prediction, providing pose, depth, confidence, Gaussians, and dynamic probability for SLAM. Going beyond its explicit predictions, we further exploit the latent cross-view geometry encoded in internal attention to perform correspondence-aware patch sampling (CAPS) for camera tracking, turning implicit geometric knowledge into reliable constraints for bundle adjustment. For static mapping, prior Gaussians are compacted through voxelization and incrementally consolidated into a persistent map according to scene coverage. For dynamic content, rather than rejecting moving observations or relying on frame-local optical flow propagation and learned deformation fields, we introduce Track-Anchored Gaussians (TAGs) that anchor canonical dynamic Gaussians to long-lived 3D motion tracks. This decouples persistent scene content from temporal motion and establishes a persistent motion scaffold across frames, enabling more stable temporal association under large motion and occlusion. The resulting system yields accurate dynamic perception, robust camera tracking, and high-quality 4D reconstruction from monocular RGB input. Experiments on challenging dynamic datasets demonstrate our method's state-of-the-art performance in both camera tracking and 4D reconstruction.