Minute4D: Training High-Fidelity 4D Gaussian Splatting in One Minute

Dynamic view synthesis has seen significant advances, yet re-constructing scenes from uncalibrated, casual videos remains challengingdue to ambiguity between camera and object motion, inadequate viewcoverage and slow optimization. In this work, we present Minute4D,a novel and efficient approach for high-fidelity 4D scene reconstructionfrom monocular videos using Gaussian Splatting. Our Gaussian initial-ization begins with an efficient geometric recovery leveraging pre-trainedvisual foundation models. To improve reconstruction fidelity, we intro-duce a Segmentation-Tracking Enhancement module that leverages 2Dsemantic priors and 3D point tracking to jointly enhance the geometricaccuracy and completeness of the initial Gaussians. To reduce redun-dancy and accelerate Gaussian optimization, we propose a Loss-GuidedDensity Control strategy that adaptively densifies and prunes Gaussiansbased on multi-view photometric loss. Our method reduces the optimiza-tion time to approximately 40 seconds for a typical 400-frame video,yielding a speed-up of at least 5×. Extensive experiments show thatour method not only enables substantially faster optimization but alsoachieves superior performance across several benchmarks.*: Equal contribution.†: Co-corresponding authors.