Novel view synthesis of dynamic scenes has been an intriguing yet challenging
problem. Despite recent advancements, simultaneously achieving high-resolution
photorealistic results, real-time rendering, and compact storage remains a
formidable task. To address these challenges, we propose Spacetime Gaussian
Feature Splatting as a novel dynamic scene representation, composed of three
pivotal components. First, we formulate expressive Spacetime Gaussians by
enhancing 3D Gaussians with temporal opacity and parametric motion/rotation.
This enables Spacetime Gaussians to capture static, dynamic, as well as
transient content within a scene. Second, we introduce splatted feature
rendering, which replaces spherical harmonics with neural features. These
features facilitate the modeling of view- and time-dependent appearance while
maintaining small size. Third, we leverage the guidance of training error and
coarse depth to sample new Gaussians in areas that are challenging to converge
with existing pipelines. Experiments on several established real-world datasets
demonstrate that our method achieves state-of-the-art rendering quality and
speed, while retaining compact storage. At 8K resolution, our lite-version
model can render at 60 FPS on an Nvidia RTX 4090 GPU.

提出了一种新的动态场景视图合成方法，通过引入时间、运动和旋转参数增强了三维高斯函数，使用神经特征进行渲染，同时在具有挑战性的区域中利用训练误差和粗略深度进行高分辨率、实时渲染，并保持紧凑存储。