  We introduce 3DEgo to address a novel problem of directly synthesizing
photorealistic 3D scenes from monocular videos guided by textual prompts.
Conventional methods construct a text-conditioned 3D scene through a
three-stage process, involving pose estimation using Structure-from-Motion
(SfM) libraries like COLMAP, initializing the 3D model with unedited images,
and iteratively updating the dataset with edited images to achieve a 3D scene
with text fidelity. Our framework streamlines the conventional multi-stage 3D
editing process into a single-stage workflow by overcoming the reliance on
COLMAP and eliminating the cost of model initialization. We apply a diffusion
model to edit video frames prior to 3D scene creation by incorporating our
designed noise blender module for enhancing multi-view editing consistency, a
step that does not require additional training or fine-tuning of T2I diffusion
models. 3DEgo utilizes 3D Gaussian Splatting to create 3D scenes from the
multi-view consistent edited frames, capitalizing on the inherent temporal
continuity and explicit point cloud data. 3DEgo demonstrates remarkable editing
precision, speed, and adaptability across a variety of video sources, as
validated by extensive evaluations on six datasets, including our own prepared
GS25 dataset. Project Page: https://3dego.github.io/
