CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion

TL;DR

This paper introduces a novel framework for controllable, consistent, and high-quality editing of dynamic 3D scenes by fine-tuning a diffusion model and optimizing scene representations based on deformable 3D Gaussians, transforming complex editing tasks into simple 2D image edits.

Contribution

The work presents a new method that enables precise local edits in dynamic 3D scenes by learning from a single reference image and employing a two-stage optimization process.

Findings

Enables consistent local edits without tracking regions.

Achieves high-quality, controllable scene modifications.

Outperforms existing methods in flexibility and quality.

Abstract

Recent advances in 3D representations, such as Neural Radiance Fields and 3D Gaussian Splatting, have greatly improved realistic scene modeling and novel-view synthesis. However, achieving controllable and consistent editing in dynamic 3D scenes remains a significant challenge. Previous work is largely constrained by its editing backbones, resulting in inconsistent edits and limited controllability. In our work, we introduce a novel framework that first fine-tunes the InstructPix2Pix model, followed by a two-stage optimization of the scene based on deformable 3D Gaussians. Our fine-tuning enables the model to "learn" the editing ability from a single edited reference image, transforming the complex task of dynamic scene editing into a simple 2D image editing process. By directly learning editing regions and styles from the reference, our approach enables consistent and precise local edits without the need for tracking desired editing regions, effectively addressing key challenges in dynamic scene editing. Then, our two-stage optimization progressively edits the trained dynamic scene, using a designed edited image buffer to accelerate convergence and improve temporal consistency. Compared to state-of-the-art methods, our approach offers more flexible and controllable local scene editing, achieving high-quality and consistent results.