3DEgo: 3D Editing on the Go!

• URL: http://arxiv.org/abs/2407.10102v1
• Date: Sun, 14 Jul 2024 07:03:50 GMT
• Title: 3DEgo: 3D Editing on the Go!
• Authors: Umar Khalid, Hasan Iqbal, Azib Farooq, Jing Hua, Chen Chen,
• Abstract summary: We introduce 3DEgo to address a novel problem of directly synthesizing 3D scenes from monocular videos guided by textual prompts. Our framework streamlines the conventional multi-stage 3D editing process into a single-stage workflow. 3DEgo demonstrates remarkable editing precision, speed, and adaptability across a variety of video sources.
• Score: 6.072473323242202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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