ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing (2411.05006v1)

Abstract: This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene editing is rooted in the diffusion model's large feasible output space (FOS), our framework controls the size of FOS and reduces inconsistency by decomposing the overall editing task into several subtasks, which are then executed progressively on the scene. Within this framework, we design a difficulty-aware subtask decomposition scheduler and an adaptive 3D Gaussian splatting (3DGS) training strategy, ensuring high quality and efficiency in performing each subtask. Extensive evaluation shows that our ProEdit achieves state-of-the-art results in various scenes and challenging editing tasks, all through a simple framework without any expensive or sophisticated add-ons like distillation losses, components, or training procedures. Notably, ProEdit also provides a new way to control, preview, and select the "aggressivity" of editing operation during the editing process.

• The paper introduces a novel progressive framework that decomposes 3D scene editing into manageable subtasks, reducing inconsistencies from large feasible output spaces.
• It utilizes an adaptive 3D Gaussian splatting representation paired with a dual-GPU setup to maintain geometric precision and improve edit quality.
• Experimental results demonstrate superior performance over methods like Instruct-NeRF2NeRF, particularly in handling substantial geometric transformations.

Insights into ProEdit: Efficient 3D Scene Editing through Progressive Frameworks

The paper "ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing" by Jun-Kun Chen and Yu-Xiong Wang from the University of Illinois Urbana-Champaign presents a novel approach to 3D scene editing. This work explores the challenges posed by large feasible output spaces (FOS) in multi-view scene editing and proposes a methodical decomposition of tasks into progressively manageable subtasks, yielding significant improvements in both efficiency and quality of 3D scene edits.

Problem Context and Motivation

The task of 3D scene editing, particularly instruction-guided scene editing (IGSE), is traditionally challenged by the inconsistencies that arise during the editing of scenes from multiple perspectives. This inconsistency is primarily attributed to the expansive FOS inherent in the diffusion models employed by state-of-the-art methods. These models, while powerful, interpret textual instructions in varied ways depending on the view, often causing inefficiencies and suboptimal convergence on the desired edited scene.

Conventional approaches aiming to address these inconsistencies have focused on robust distillation losses and supplementary components to strengthen image selection from the large FOS, albeit at the cost of added complexity and resource consumption. However, in the face of particularly large FOS, these methods struggle to deliver high-quality scene edits, especially when significant geometric adjustments are needed.

ProEdit Framework and Methodology

ProEdit addresses these challenges by discerningly controlling the FOS size through the decomposition of the editing task into subtasks, effectively reducing the complexity involved at each stage. This reduction is accomplished via interpolation-based task decomposition, segmenting the principal editing goal into smaller, more defined subtasks with controlled FOS.

This method facilitates ease of task execution, bringing a notable improvement in the potential to converge on high-quality edits without recourse to additional distillation losses or complex training regimens. The ProEdit process is anchored on a subtask scheduler, which adaptively determines the complexity of each subtask based on estimated FOS sizes and progressively applies these subtasks on the scene.

Aiding in maintaining geometric precision, the authors employ an adaptive 3D Gaussian splatting (3DGS) scene representation. This system introduces novel strategies to control Gaussian creation, crucial for refining and sustaining geometry as edits accrue through subtasks. The dual-GPU setup further enhances efficiency, segregating diffusion model inference from 3DGS training to maximize resource utilization.

Experimental Validation and Comparative Analysis

The researchers extensively test their framework against several advanced methods, such as Instruct-NeRF2NeRF and ConsistDreamer, across a spectrum of datasets and editing tasks. The results underscore ProEdit’s superior capacity to generate high-fidelity edits with accurate geometric details and vibrant textures, asserting its competitiveness and state-of-the-art performance in 3D scene editing.

Notably, ProEdit excels in tasks requiring substantial geometric transformations, which have historically been problematic for existing methods. A key feature of ProEdit is its capability to allow users to control and select "editing aggressivity," enabling evaluations of various editing intensities throughout the editing process.

Implications for Future Developments

ProEdit’s approach to managing FOS and task decomposition lays extensive groundwork for refining 3D scene editing methodologies. This framework not only underscores the importance of progressive editing but also introduces avenues for enhancing user interactivity and control over the editing process.

The progressive framework can potentially extend beyond static 3D editing to dynamic scenes or even 4D (spatiotemporal) scene edits. Future research could aim to combine ProEdit with 3D consistency enhancers or explore integration with scene generation applications, paving the way for broader capabilities in AI-powered graphical design and animation.

In conclusion, this work epitomizes a significant step towards resolving fundamental challenges in high-quality 3D scene editing, equipping practitioners with a robust framework to produce detailed and consistent edits efficiently. The results from ProEdit present a promising horizon for innovations in AI-driven 3D content creation and manipulation.