Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting

TL;DR

This paper introduces a novel 4D Gaussian Splatting method for real-time, photorealistic rendering of dynamic scenes from 2D images, effectively capturing complex motions and structures with explicit geometry and appearance modeling.

Contribution

It proposes a simple, flexible 4D primitive-based approach that models spatio-temporal scene volume for high-quality, real-time dynamic scene rendering, surpassing previous neural implicit methods.

Findings

Superior visual quality on various benchmarks

Efficient real-time rendering capability

Effective modeling of complex scene dynamics

Abstract

Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics. Despite advancements in neural implicit models, limitations persist: (i) Inadequate Scene Structure: Existing methods struggle to reveal the spatial and temporal structure of dynamic scenes from directly learning the complex 6D plenoptic function. (ii) Scaling Deformation Modeling: Explicitly modeling scene element deformation becomes impractical for complex dynamics. To address these issues, we consider the spacetime as an entirety and propose to approximate the underlying spatio-temporal 4D volume of a dynamic scene by optimizing a collection of 4D primitives, with explicit geometry and appearance modeling. Learning to optimize the 4D primitives enables us to synthesize novel views at any desired time with our tailored rendering routine. Our model is conceptually simple, consisting of a 4D Gaussian parameterized by anisotropic ellipses that can rotate arbitrarily in space and time, as well as view-dependent and time-evolved appearance represented by the coefficient of 4D spherindrical harmonics. This approach offers simplicity, flexibility for variable-length video and end-to-end training, and efficient real-time rendering, making it suitable for capturing complex dynamic scene motions. Experiments across various benchmarks, including monocular and multi-view scenarios, demonstrate our 4DGS model's superior visual quality and efficiency.