Optimized Minimal 4D Gaussian Splatting
Authors: name: Minseo Lee, name: Byeonghyeon Lee, name: Lucas Yunkyu Lee, name: Eunsoo Lee, name: Sangmin Kim, name: Seunghyeon Song, name: Joo Chan Lee, name: Jong Hwan Ko, name: Jaesik Park, name: Eunbyung Park

4D Gaussian Splatting has emerged as a new paradigm for dynamic scene
representation, enabling real-time rendering of scenes with complex motions.
However, it faces a major challenge of storage overhead, as millions of
Gaussians are required for high-fidelity reconstruction. While several studies
have attempted to alleviate this memory burden, they still face limitations in
compression ratio or visual quality. In this work, we present OMG4 (Optimized
Minimal 4D Gaussian Splatting), a framework that constructs a compact set of
salient Gaussians capable of faithfully representing 4D Gaussian models. Our
method progressively prunes Gaussians in three stages: (1) Gaussian Sampling to
identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning
to remove redundancies, and (3) Gaussian Merging to fuse primitives with
similar characteristics. In addition, we integrate implicit appearance
compression and generalize Sub-Vector Quantization (SVQ) to 4D representations,
further reducing storage while preserving quality. Extensive experiments on
standard benchmark datasets demonstrate that OMG4 significantly outperforms
recent state-of-the-art methods, reducing model sizes by over 60% while
maintaining reconstruction quality. These results position OMG4 as a
significant step forward in compact 4D scene representation, opening new
possibilities for a wide range of applications. Our source code is available at
https://minshirley.github.io/OMG4/.