TOM-GS: Editable Video Representation via Temporal Opacity Modulation of Static 3D Gaussians
While Implicit Neural Representations (INRs) and dynamic 3D Gaussian Splatting (3DGS) achieve impressive results in video processing, they often fall short of producing representations that are easily editable. Recent methods address this by introducing complex spatial deformations or folded distributions, which constrain optimization and reduce flexibility for downstream editing. In this paper, we introduce TOM-GS, an editable video representation that forgoes complex deformations in favor of regular 3D Gaussians equipped with a continuous temporal opacity formulation. By assigning a learnable temporal mean and scale to the opacity of each Gaussian, our model enables static 3D spatial co...
| Authors | Marek Lisowski, Łukasz Smoliński, Kornel Howil et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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