4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization

Understand

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality.

• We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular videos.
• To overcome the overfitting problem of existing work for these real-world videos, we introduce an uncertainty-aware regularization that identifies uncertain regions with few observations and selectively imposes additional priors based on diffusion models and depth smoothness on such regions.
• This approach improves both the performance of novel view synthesis and the quality of training image reconstruction.