A Compact Dynamic 3D Gaussian Representation for Real-Time Dynamic View Synthesis

Kai Katsumata*, Duc Minh Vo, Hideki Nakayama ;

"3D Gaussian Splatting (3DGS) has shown remarkable success in synthesizing novel views given multiple views of a static scene. Yet, 3DGS faces challenges when applied to dynamic scenes because 3D Gaussian parameters need to be updated per timestep, requiring a large amount of memory and at least a dozen observations per timestep. To address these limitations, we present a compact dynamic 3D Gaussian representation that models positions and rotations as functions of time with a few parameter approximations while keeping other properties of 3DGS including scale, color, and opacity invariant. Our method can dramatically reduce memory usage and relax a strict multi-view assumption. In our experiments on monocular and multi-view scenarios, we show that our method not only matches state-of-the-art methods, often linked with slower rendering speeds, in terms of high rendering quality, but also significantly surpasses them by achieving a rendering speed of 118 frames per second at a resolution of 1,352×1,014 on a single GPU."