Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation

Rui Wang; Quentin Lohmeyer; Siyu Tang; Mirko Meboldt

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arXiv · 2606.22197

Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation

Rui Wang·Quentin Lohmeyer·Siyu Tang·Mirko Meboldt

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Abstract

Dynamic 3D Gaussian splatting faces a fundamental tension between motion consistency and visual fidelity. Deformation-based approaches preserve temporal correspondence but suffer from motion over-factorization, oversmoothing high-frequency dynamics. In contrast, 4D-primitive methods capture fine visual details yet incur temporal overparameterization, breaking object identity and leading to severe storage overhead. To resolve this, we introduce Multi4D, a framework for high-fidelity dynamic Gaussian Splatting based on multi-level competitive allocation. Instead of a monolithic representation, we distribute modeling capacity across three structured levels: static structure, persistent dynamic geometry, and transient appearance primitives. Through shared rasterization and residual-driven optimization, these levels dynamically compete to explain photometric error, enabling adaptive specialization without pre-assigned decomposition. This allocation preserves long-term motion consistency while capturing fine dynamic detail, achieving state-of-the-art rendering quality and real-time performance with significantly fewer dynamic primitives. Furthermore, because our representation explicitly tracks compact persistent Gaussians over time, semantic features can be embedded afterward, enabling Multi4D to achieve state-of-the-art 4D segmentation accuracy with an order-of-magnitude speedup. Project page: https://batfacewayne.github.io/Multi4D.io/

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Rui Wang, Quentin Lohmeyer, Siyu Tang, Mirko Meboldt. 2026-06-20. Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation. https://arxiv.org/abs/2606.22197

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