  Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes
from multi-view videos remains a challenging endeavor. Despite the remarkable
advancements achieved by current neural rendering techniques, these methods
generally require complete video sequences for offline training and are not
capable of real-time rendering. To address these constraints, we introduce
3DGStream, a method designed for efficient FVV streaming of real-world dynamic
scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12
seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D
Gaussians (3DGs) to represent the scene. Instead of the na\"ive approach of
directly optimizing 3DGs per-frame, we employ a compact Neural Transformation
Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing
the training time and storage required for each FVV frame. Furthermore, we
propose an adaptive 3DG addition strategy to handle emerging objects in dynamic
scenes. Experiments demonstrate that 3DGStream achieves competitive performance
in terms of rendering speed, image quality, training time, and model storage
when compared with state-of-the-art methods.
